Techniques for static handwriting trajectory recovery: a survey
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paper_title: Handwritten character skeletonisation for forensic document analysis

paper_content:

A new method of skeletonisation (stroke extraction) of handwritten character images is presented. The method has been designed to extract the skeleton which is very close to human perception of the original pen tip trajectory. The need in such skeletonisation arises from feature extraction algorithms which are sensitive to inaccuracies in positions of skeleton curves. One class of such algorithms are those for extraction of features used in forensic analysis of handwriting. The skeleton is constructed in three steps directly from the grayscale image and is represented as a set of curves, which, in turn, are represented as cubic B-splines. Such representation also eases feature extraction.Experiments have been performed on 150 images of grapheme "th" written by different writers. The assessment of the skeletonisation results are presented.
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paper_title: Recovery of temporal information from static images of handwriting

paper_content:

A taxonomy of local, regional, and global temporal clues that, along with a detailed examination of the document, allow temporal properties to be recovered from the image is provided. It is shown that this system will benefit from obtaining a comprehensive understanding of the handwriting signal and that it requires a detailed analysis of stroke and sub-stroke properties. It is suggested that this task requires breaking away from traditional thresholding and thinning techniques, and a framework for such analysis is presented. It is shown how the temporal clues can reliably be extracted from this framework and how many of the seemingly ambiguous situations can be resolved by the derived clues and knowledge of the writing process. >
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paper_title: Perception of Dynamic Information in Static Handwritten Forms

paper_content:

Handwriting recognition presents a problem for most theories of letter perception. How do people accurately discriminate letters given the variability in handwritten forms? We propose that perceivers detect and capitalize on information relating to production inherent in the static trace of the handwritten letter. In an "implicit detection" task, subjects' ability to detect stroke direction in a series of handwritten characters was found to be in-
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paper_title: Recovery of temporal information of cursively handwritten words for on-line recognition

paper_content:

On-line recognition differs from off-line recognition in that additional information about the drawing order of the strokes is available. This temporal information makes it easier to recognize handwritten texts with an on-line recognition system. In this paper we present a method for the recovery of the stroke order from static handwritten images. The algorithm was tested by classifying the words of an off-line database with a state-of-the-art on-line recognition system. On this database with 150 different words, written by four cooperative writers, a recognition rate of 97.4% was obtained.
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paper_title: Visual identification by signature tracking

paper_content:

We propose a new camera-based biometric: visual signature identification. We discuss the importance of the parameterization of the signatures in order to achieve good classification results, independently of variations in the position of the camera with respect to the writing surface. We show that affine arc-length parameterization performs better than conventional time and Euclidean arc-length ones. We find that the system verification performance is better than 4 percent error on skilled forgeries and 1 percent error on random forgeries, and that its recognition performance is better than 1 percent error rate, comparable to the best camera-based biometrics.
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paper_title: Offline Signature Verification Using Online Handwriting Registration

paper_content:

This paper proposes a novel framework for offline signature verification. Different from previous methods, our approach makes use of online handwriting instead of handwritten images for registration. The online registrations enable robust recovery of the writing trajectory from an input offline signature and thus allow effective shape matching between registration and verification signatures. In addition, we propose several new techniques to improve the performance of the new signature verification system: 1. we formulate and solve the recovery of writing trajectory within the framework of conditional random fields; 2. we propose a new shape descriptor, online context, for aligning signatures; 3. we develop a verification criterion which combines the duration and amplitude variances of handwriting. Experiments on a benchmark database show that the proposed method significantly outperforms the well-known offline signature verification methods and achieve comparable performance with online signature verification methods.
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paper_title: Temporal clues in handwriting

paper_content:

Handwritten character recognition is typically classified as online or offline depending on the nature of the input data. Online data consists of a temporal sequence of instrument positions while offline data is in the form of a 2D image of the writing sample. Online recognition techniques have been relatively successful but have the disadvantage of requiring the data to be gathered during the writing process. This paper presents work on the extraction of temporal information from static images of handwriting and its implications for character recognition. >
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paper_title: Handwritten numeral recognition based on hierarchically self-organizing learning networks

paper_content:

Proposes a novel approach to tracing, representation, and subsequently recognition of handwritten numerals. The proposed approach extracts the geometrical and topological features of a numeral and, more importantly, provides the temporal (or dynamic) relationship among the strokes using a heuristic-rule-based tracing algorithm capable of generating a typical stroke sequence of a numeral. With the stroke sequence identified, one is able to extract the feature points (called critical points) of each stroke in an order given by the tracing sequence such that both static features, such as geometrical and topological features, and dynamical features, such as the temporal relationship among strokes, the number of strokes, and the direction of starting and ending strokes, can be preserved. Utilizing the temporal relationship among critical points and their corresponding X (or Y) coordinates as inputs and outputs, one can train a new neural network architecture using a supervised learning algorithm, referred to as a hierarchically self-organizing learning network, as a novel approach to handwritten numeral recognition. >
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paper_title: Recognition-directed recovering of temporal information from handwriting images

paper_content:

This paper analyses a handwriting recognition system for offline cursive words based on HMMs. It compares two approaches for transforming offline handwriting available as two-dimensional images into one-dimensional input signals that can be processed by HMMs. In the first approach, a left-right scan of the word is performed resulting in a sequence of feature vectors. In the second approach, a more subtle process attempts to recover the temporal order of the strokes that form words as they were written. This is accomplished by a graph model that generates a set of paths, each path being a possible temporal order of the handwriting. The recognition process then selects the most likely temporal stroke order based on knowledge that has been acquired from a large set of handwriting samples for which the temporal information was available. We show experimentally that such an offline recognition system using the recovered temporal order can achieve recognition performances that are much better than those obtained with the simple left-right order, and that come close to those of an online recognition system. We have been able to assess the ordering quality of handwriting when comparing true ordering and recovered one, and we also analyze the situations where offline and online information differ and what the consequences are on the recognition performances. For these evaluations, we have used about 30,000 words from the IRONOFF database that features both the online signal and offline signal for each word.
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paper_title: FROM OFF-LINE TO ON-LINE HANDWRITING RECOGNITION

paper_content:

On-line handwriting includes more information on the time order of the writing signal and on the dynamics of the writing process than off-line handwriting. Therefore, on-line recognition systems achieve higher recognition rates. This can be concluded from results reported in the literature, and has been demonstrated empirically as part of this work. We propose a new approach for recovering the time order of the off-line writing signal. Starting from an over-segmentation of the off-line handwriting into regular and singular parts, the time ordering of these parts and recognition of the word are performed simultaneously. This approach, termed “ OrdRec”, is based on a graph description of the handwriting signal and a recognition process using Hidden Markov Models (HMM). A complete omni-scriptor isolated word recognition system has been developed. Using a dynamic lexicon and models for upper and lower case characters, our system can process binary and gray value word images of any writing style (script, cursive, or mixed). Using a dual handwriting data base which features both the on-line and the off-line signal for each of the 30 000 words written by about 700 scriptors, we have shown experimentally that such an off-line recognition system, using the recovered time order information, can achieve recognition performances close to those of an on-line recognition system.
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paper_title: Identification of fork points on the skeletons of handwritten Chinese characters

paper_content:

This paper describes techniques for stroke extraction used in the recognition of handwritten Chinese characters. A new set of feature points is proposed for the analysis of skeleton images. Based on a geometrical graph, a novel criterion is proposed for the identification of fork points in a skeleton image which correspond to joint points in the original character image. Experimental results indicate that the proposed method correctly determines the fork points, and is effective in unifying the joint points.
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paper_title: Offline Signature Verification System Based on the Online Data

paper_content:

Most of the signature verification work done in the past years focused either on offline or online approaches. In this paper, a different methodology is proposed, where the online reference data acquired through a digitizing tablet serves as the basis for the segmentation process of the corresponding scanned offline data. Local windows are built over the image through a self-adjustable learning process and are used to focus on the feature extraction step. The window's positions are determined according to the complexity of the underlying strokes based on the observation of a delta-lognormal handwritten reproduction model. Local features extraction that takes place focused on the windows formed, and it is used in conjunction with the global primitives to feed the classifier. The overall performance of the system is then measured with three different classification schemes.
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paper_title: Arc-based evaluation and detection of ellipses

paper_content:

We propose the use of elliptic arcs as a reliable criterion for ellipse evaluation and reconstruction. A technique based on pixel connectivity is developed for detecting the endpoints and subtended angles of elliptic arcs. Model-based distance and angular connectivity are introduced for quick and meaningful estimation of elliptic arcs. An iterative algorithm for multiple-ellipse fitting based on arc detection is presented. Experimental results confirm the robustness and accuracy of the algorithm for the fitting of multiple overlapping ellipses.
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paper_title: Handwritten character skeletonisation for forensic document analysis

paper_content:

A new method of skeletonisation (stroke extraction) of handwritten character images is presented. The method has been designed to extract the skeleton which is very close to human perception of the original pen tip trajectory. The need in such skeletonisation arises from feature extraction algorithms which are sensitive to inaccuracies in positions of skeleton curves. One class of such algorithms are those for extraction of features used in forensic analysis of handwriting. The skeleton is constructed in three steps directly from the grayscale image and is represented as a set of curves, which, in turn, are represented as cubic B-splines. Such representation also eases feature extraction.Experiments have been performed on 150 images of grapheme "th" written by different writers. The assessment of the skeletonisation results are presented.
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paper_title: Recovery of temporal information from static images of handwriting

paper_content:

A taxonomy of local, regional, and global temporal clues that, along with a detailed examination of the document, allow temporal properties to be recovered from the image is provided. It is shown that this system will benefit from obtaining a comprehensive understanding of the handwriting signal and that it requires a detailed analysis of stroke and sub-stroke properties. It is suggested that this task requires breaking away from traditional thresholding and thinning techniques, and a framework for such analysis is presented. It is shown how the temporal clues can reliably be extracted from this framework and how many of the seemingly ambiguous situations can be resolved by the derived clues and knowledge of the writing process. >
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paper_title: Recovery of drawing order from single-stroke handwriting images

paper_content:

Describes a method to recover a drawing order of a handwritten script from a static 2D image. The script should be written in a single stroke and may include double-traced lines. After the script is scanned in and preprocessed, we apply our recovery method which consists of two phases. In the first phase, we globally analyze the graph constructed from the skeletal image and label the graph by determining the types of each edge. In the second phase, we trace the graph from the start vertex to the end vertex using the labeling information. This method does not enumerate the possible cases, for example, by solving the traveling salesman problem and, therefore, does not cause a combinatorial explosion even if the script is very complex. By recovering a drawing order of a handwritten script, the temporal information can be recovered from a static 2D image. Hence, this method will be used as a bridge from the offline handwriting character recognition problem to the online one.
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paper_title: A pseudo-skeletonization algorithm for static handwritten scripts

paper_content:

This paper describes a skeletonization approach that has desirable characteristics for the analysis of static handwritten scripts. We concentrate on the situation where one is interested in recovering the parametric curve that produces the script. Using Delaunay tessellation techniques where static images are partitioned into sub-shapes, typical skeletonization artifacts are removed, and regions with a high density of line intersections are identified. An evaluation protocol, measuring the efficacy of our approach is described. Although this approach is particularly useful as a pre-processing step for algorithms that estimate the pen trajectories of static signatures, it can also be applied to other static handwriting recognition techniques.
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paper_title: Handwritten numeral recognition based on hierarchically self-organizing learning networks

paper_content:

Proposes a novel approach to tracing, representation, and subsequently recognition of handwritten numerals. The proposed approach extracts the geometrical and topological features of a numeral and, more importantly, provides the temporal (or dynamic) relationship among the strokes using a heuristic-rule-based tracing algorithm capable of generating a typical stroke sequence of a numeral. With the stroke sequence identified, one is able to extract the feature points (called critical points) of each stroke in an order given by the tracing sequence such that both static features, such as geometrical and topological features, and dynamical features, such as the temporal relationship among strokes, the number of strokes, and the direction of starting and ending strokes, can be preserved. Utilizing the temporal relationship among critical points and their corresponding X (or Y) coordinates as inputs and outputs, one can train a new neural network architecture using a supervised learning algorithm, referred to as a hierarchically self-organizing learning network, as a novel approach to handwritten numeral recognition. >
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paper_title: Off-Line Signature Verification Based on Feature Matching

paper_content:

This paper presents a novel approach for off-line signature verification. First the different aspects and problems of signature verification are discussed in conjunction with off-line analysis methods. It is shown, that on-line analysis methods perform usually better than off-line methods because they can make use of the temporal information (and thereby get a better perception of the semantics of the signature). An assumption is made, that off-line verification methods have difficulties improving their results because of the high level of abstraction they use, which is a direct consequence of the missing semantics. Afterwards a solution is presented with the ability to preserve some semantic information, and thereby deliver a better ability to analyze the results and optimize the system.
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paper_title: Offline tracing and representation of signatures

paper_content:

An approach for representing signatures in an offline environment is presented. The approach first makes a tracing of a signature similar to the way a human normally does, using hierarchical decision-making for stroke identification and ordering based on a set of heuristic rules. The dynamic information from the tracing sequence is then incorporated into the representation of the signature. A multiresolution critical-point segmentation method is used to extract local feature points, at varying degrees of scale and coarseness, for subsequent representation of the signature. Experimental results are discussed. >
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paper_title: Handwritten numeral recognition based on hierarchically self-organizing learning networks with spatio-temporal pattern representation

paper_content:

An approach for tracing, representation, and recognition of a handwritten numeral in an offline environment is presented. A 2D spatial representation of a numeral is first transformed into a 3D spatiotemporal representation by identifying the tracing sequence based on a set of heuristic rules acting as transformation operators. Given the dynamic information of the tracing sequence, a multiresolution critical-point segmentation method is proposed to extract local feature points, at varying degrees of scale and coarseness. A neural network architecture, the hierarchically self-organizing learning (HSOL) network (S. Lee, J.C. Pan, 1989), especially for handwritten numeral recognition, is presented. Experimental results based on a bidirectional HSOL network indicated that the method is robust in terms of variations, deformations, and corruption, achieving about 99% recognition rate for the test patterns. >
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paper_title: Piecewise Linear Skeletonization Using Principal Curves

paper_content:

Proposes an algorithm to find piecewise linear skeletons of handwritten characters by using principal curves. The development of the method was inspired by the apparent similarity between the definition of principal curves (smooth curves which pass through the "middle" of a cloud of points) and medial axes (smooth curves that run equidistantly from the contours of a character image). The central fitting-and-smoothing step of the algorithm is an extension of the polygonal line algorithm, which approximates principal curves of data sets by piecewise linear curves. The polygonal line algorithm is extended to find principal graphs and complemented with two steps specific to the task of skeletonization: an initialization method to capture the approximate topology of the character, and a collection of restructuring operations to improve the structural quality of the skeleton produced by the initialization method. An advantage of our approach over existing methods is that we optimize the skeleton graph by minimizing an intuitive and explicit objective function that captures the two competing criteria of smoothing the skeleton and fitting it closely to the pixels of the character image. We tested the algorithm on isolated handwritten digits and images of continuous handwriting. The results indicated that the proposed algorithm can find a smooth medial axis in the great majority of a wide variety of character templates and that it substantially improves the pixel-wise skeleton obtained by traditional thinning methods.
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paper_title: A total stroke SLALOM method for searching for the optimal drawing order of off-line handwriting

paper_content:

Handwriting is easier to recognize when it is done online than when it is done off-line, because for online handwriting one can use the time information obtained during the writing process, whereas for off-line handwriting one must depend solely on the static 2D image, in which the time information has already been lost. We propose in this paper a novel approach called the total stroke SLALOM (TSS) method for recovering the time information from a static 2D image of single-stroke cursive script. To cope with the increased computational burden resulting from the complexity of the script, we divide the image into small components and rely on the principle of "divide and conquer".
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paper_title: A Special Skeletonization Algorithm For Cursive Words

paper_content:

We present a novel approach for finding a pseudo­skeleton of a cursive word\\'s image. This pseudo­skeleton preserves all the necessary components of a cursive word such as: loops, curves, junctions, end­points etc. It is expected to be useful for cursive word recognition
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paper_title: An evaluation of parallel thinning algorithms for character recognition

paper_content:

Skeletonization algorithms have played an important role in the preprocessing phase of OCR systems. In this paper we report on the performance of 10 parallel thinning algorithms from this perspective by gathering statistics from their performance on large sets of data and examining the effects of the different thinning algorithms on an OCR system. >
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paper_title: A Framework Toward Restoration of Writing Order from Single-Stroked Handwriting Image

paper_content:

Restoration of writing order from a single-stroked handwriting image can be seen as the problem of finding the smoothest path in its graph representation. In this paper, a 3-phase approach to restore a writing order is proposed within the framework of the edge continuity relation (ECR). In the initial, local phase, in order to obtain possible ECRs at an even-degree node, a neural network is used for the node of degree 4 and a theoretical approach is presented for the node of degree higher than 4 by introducing certain reasonable assumptions. In the second phase, we identify double-traced lines by employing maximum weighted matching. This makes it possible to transform the problem of obtaining possible ECRs at odd-degree node to that at even-degree node. In the final, global phase, we find all the candidates of single-stroked paths by depth first search and select the best one by evaluating SLALOM smoothness. Experiments on static images converted from online data in the Unipen database show that our method achieves a restoration rate of 96.0 percent
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paper_title: Extraction of strokes in handwritten characters

paper_content:

Abstract Among the many handwritten character recognition algorithms that have been proposed in the past few years, few of them use models which are able to simulate handwriting. This can be explained by the fact that simulation models require the estimation of strokes starting from statistic images of letters, while crossing and overlapping strokes make this estimation difficult. The approach we suggest is to efficiently deal with crossing areas and overlaps using parametric representations of lines and thickness of stroke: a probabilistic model of strokes is described to extract non-overlapping strokes of the image. A bayesian approach using a statistical study and a model of stroke crossing is described that optimizes the reconstruction of crossings and permits to characterize image of letters by robust graphs of curves.
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paper_title: Thinning methodologies−a comprehensive survey

paper_content:

A comprehensive survey of thinning methodologies is presented. A wide range of thinning algorithms, including iterative deletion of pixels and nonpixel-based methods, is covered. Skeletonization algorithms based on medial axis and other distance transforms are not considered. An overview of the iterative thinning process and the pixel-deletion criteria needed to preserve the connectivity of the image pattern is given first. Thinning algorithms are then considered in terms of these criteria and their modes of operation. Nonpixel-based methods that usually produce a center line of the pattern directly in one pass without examining all the individual pixels are discussed. The algorithms are considered in great detail and scope, and the relationships among them are explored. >
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paper_title: Hidden loop recovery for handwriting recognition

paper_content:

One significant challenge in the recognition of off-line handwriting is in the interpretation of loop structures. Although this information is readily available in online representation, close proximity of strokes often merges their centers making them difficult to identify. In this paper a novel approach to the recovery of hidden loops in off-line scanned document images is presented. The proposed algorithm seeks blobs that resemble truncated ellipses. We use a sophisticated form analysis method based on mutual distance measurements between the two sides of a symmetric shape. The experimental results are compared with the ground truth of the online representations of each off-line word image. More than 86% percent of the meaningful loops are handled correctly.
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paper_title: Offline Loop Investigation for Handwriting Analysis

paper_content:

Resolution of different types of loops in handwritten script presents a difficult task and is an important step in many classic word recognition systems, writer modeling, and signature verification. When processing a handwritten script, a great deal of ambiguity occurs when strokes overlap, merge, or intersect. This paper presents a novel loop modeling and contour-based handwriting analysis that improves loop investigation. We show excellent results on various loop resolution scenarios, including axial loop understanding and collapsed loop recovery. We demonstrate our approach for loop investigation on several realistic data sets of static binary images and compare with the ground truth of the genuine online signal.
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paper_title: Use of exterior contours and shape features in off-line signature verification

paper_content:

An approach to off-line signature verification, one with an on-line flavor, is described. A sequence of data is obtained by tracing the exterior contour of the signature which allows the application of string-matching algorithms. The upper and lower contours of the signature are first determined by ignoring small gaps between signature components. The contours are combined into a single sequence so as to define a pseudo-writing path. To match two signatures a non-linear normalization method, viz., dynamic time warping, is applied to segment them into curves. Shape descriptors based on Zernike moments are extracted as features from each segment. A harmonic distance is used for measuring signature similarity. Performance is significantly better than that of a word-shape based signature verification method. When the two methods are combined, the overall performance is significantly better than either method alone. With a database of 1320 genuines and 1320 forgeries the combination method has an accuracy of 95% (with 20% rejection) which is comparable to that of on-line systems.
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paper_title: Extraction and analysis of document examiner features from vector skeletons of grapheme ‘th'

paper_content:

This paper presents a study of 25 structural features extracted from samples of grapheme ‘th' that correspond to features commonly used by forensic document examiners. Most of the features are extracted using vector skeletons produced by a specially developed skeletonisation algorithm. The methods of feature extraction are presented along with the results. Analysis of the usefulness of the features was conducted and three categories of features were identified: indispensable, partially relevant and irrelevant for determining the authorship of genuine unconstrained handwriting. The division was performed based on searching the optimal feature sets using the wrapper method. A constructive neural network was used as a classifier and a genetic algorithm was used to search for optimal feature sets. It is shown that structural micro features similar to those used in forensic document analysis do possess discriminative power. The results are also compared to those obtained in our preceding study, and it is shown that use of the vector skeletonisation allows both extraction of more structural features and improvement the feature extraction accuracy from 87% to 94%.
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paper_title: Time-efficient stroke extraction method for handwritten signatures

paper_content:

Off-line signature verification methods have to tackle with a number of high resolution images, therefore it is vital to use efficient image processing algorithms. This paper proposes a heuristic stroke extraction algorithm to create a compact representation of the signature whilst minimizing the loss of semantic information and the computational needs. Some issues of the algorithm are highlighted and - as far as possible - also eliminated. Finally an experimental validation of the algorithm is presented and evaluated. This algorithm could be used to refine existing signature verification methods, and also to create new ones.
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paper_title: Document image understanding: integrating recovery and interpretation

paper_content:

Many document image understanding problems require a more comprehensive examination of document features than is typically deemed necessary for recognition tasks. We believe that these problems require a detailed analysis of stroke and sub-stroke features in the document image with the goal of obtaining information about the environment or process which created the document and establishing a context for understanding. ::: We introduce the concept of recovery into the document domain. We provide a "stroke platform" representation which establishes a verifiable "link to the pixels" and demonstrate its usefulness for recovery tasks. This representation allows us to overcome many of the problems associated with the rapid, irreversible abstraction associated with traditional document processing methods and provides the basic framework for our analysis of handwritten documents. By obtaining a detailed description of the document and its properties, we are able to establish a context for analysis and validate assumptions about the domain. This dissertation presents our work on several document image understanding problems: (1) demonstrating the successful use of the stroke platform for the problem of interpreting and reconstructing junctions and endpoints, (2) exploring the effects of the handwriting process on the document by the development of a model for instrument grasp and a study of its effects on pressure features, (3) posing and providing an approach to the problem of recovering temporal information from static images of handwriting, (4) addressing various sub-tasks of the problem of processing form documents, and (5) extending the detailed analysis philosophy to demonstrate its feasibility in related document domains.
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paper_title: Recovery of temporal information from static images of handwriting

paper_content:

A taxonomy of local, regional, and global temporal clues that, along with a detailed examination of the document, allow temporal properties to be recovered from the image is provided. It is shown that this system will benefit from obtaining a comprehensive understanding of the handwriting signal and that it requires a detailed analysis of stroke and sub-stroke properties. It is suggested that this task requires breaking away from traditional thresholding and thinning techniques, and a framework for such analysis is presented. It is shown how the temporal clues can reliably be extracted from this framework and how many of the seemingly ambiguous situations can be resolved by the derived clues and knowledge of the writing process. >
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paper_title: A Comparative Study on 2D Curvature Estimators

paper_content:

Curvature is a frequently used property in two-dimensional (2D) shape analysis, directly or for derived features such as corners or convex and concave arcs. This paper presents curvature estimators which follow approaches in differential geometry. Digital-straight segment approximation (as known from digital geometry) is used in those estimators. Results of multigrid experiments are evaluated leading to a comparative performance analysis of several curvature estimators
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paper_title: A total stroke SLALOM method for searching for the optimal drawing order of off-line handwriting

paper_content:

Handwriting is easier to recognize when it is done online than when it is done off-line, because for online handwriting one can use the time information obtained during the writing process, whereas for off-line handwriting one must depend solely on the static 2D image, in which the time information has already been lost. We propose in this paper a novel approach called the total stroke SLALOM (TSS) method for recovering the time information from a static 2D image of single-stroke cursive script. To cope with the increased computational burden resulting from the complexity of the script, we divide the image into small components and rely on the principle of "divide and conquer".
---
paper_title: A Framework Toward Restoration of Writing Order from Single-Stroked Handwriting Image

paper_content:

Restoration of writing order from a single-stroked handwriting image can be seen as the problem of finding the smoothest path in its graph representation. In this paper, a 3-phase approach to restore a writing order is proposed within the framework of the edge continuity relation (ECR). In the initial, local phase, in order to obtain possible ECRs at an even-degree node, a neural network is used for the node of degree 4 and a theoretical approach is presented for the node of degree higher than 4 by introducing certain reasonable assumptions. In the second phase, we identify double-traced lines by employing maximum weighted matching. This makes it possible to transform the problem of obtaining possible ECRs at odd-degree node to that at even-degree node. In the final, global phase, we find all the candidates of single-stroked paths by depth first search and select the best one by evaluating SLALOM smoothness. Experiments on static images converted from online data in the Unipen database show that our method achieves a restoration rate of 96.0 percent
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paper_title: Digital curvature estimation

paper_content:

Abstract This paper sets out to study the effect of digitization on curvature. It is put forward that digital curvature is an estimate with accuracy and precision. A theoretical analysis of the curvature estimation problem identifies the possible sources of errors in digital curvature estimation. In the literature, five essentially different classes of methods are found. From theoretical analysis of the methods, as well as by random experiments on generated arcs, we establish that almost all existing methods suffer from a severe directional inaccuracy and/or poor precision. Errors depend on the method, orientation, and scale, ranging from 1% to over 1000%. We present a practical solution with a residual error between 1% and 60%, also giving recommendations for the required resolution of the image.
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paper_title: Document image understanding: integrating recovery and interpretation

paper_content:

Many document image understanding problems require a more comprehensive examination of document features than is typically deemed necessary for recognition tasks. We believe that these problems require a detailed analysis of stroke and sub-stroke features in the document image with the goal of obtaining information about the environment or process which created the document and establishing a context for understanding. ::: We introduce the concept of recovery into the document domain. We provide a "stroke platform" representation which establishes a verifiable "link to the pixels" and demonstrate its usefulness for recovery tasks. This representation allows us to overcome many of the problems associated with the rapid, irreversible abstraction associated with traditional document processing methods and provides the basic framework for our analysis of handwritten documents. By obtaining a detailed description of the document and its properties, we are able to establish a context for analysis and validate assumptions about the domain. This dissertation presents our work on several document image understanding problems: (1) demonstrating the successful use of the stroke platform for the problem of interpreting and reconstructing junctions and endpoints, (2) exploring the effects of the handwriting process on the document by the development of a model for instrument grasp and a study of its effects on pressure features, (3) posing and providing an approach to the problem of recovering temporal information from static images of handwriting, (4) addressing various sub-tasks of the problem of processing form documents, and (5) extending the detailed analysis philosophy to demonstrate its feasibility in related document domains.
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paper_title: Recovery of temporal information from off-line Arabic handwritten

paper_content:

Summary form only given. In this paper, we present a system of restoration of temporal order in the offline Arabic handwritten tracing. The word image, captured in level of gray from a scanner with a resolution of 300 dpi, passes by four stages of preprocessing: binarization, filtering, smoothing and elimination of the diacritical signs. A first algorithm calculates for every point of the contour a curvature function. The local maximas of this function correspond to the dominant points of the contour. The dominant points correspond to all angular variations on the contour (end of the stroke, cross point, branch point, pocket, buckle...). A sweep of a window of a dimension equal to the triple of the thickness, of the stroke permits to detect cross and branch points of features and a suitable algorithm permits to solve the problem of the temporal order in these ambiguous zones. Finally, another algorithm permits to detect the starting point and to follow the tracing in order to reconstitute the order in which it has been written. The system is improved by an algorithm that treats the particular cases concerning the starting point, the ambiguous zone analysis and the progression of the system at the time of the follow-up of the contour. Preliminary results obtained from our basis (50 words written by 2 people), are: detection of the point of departure: 96%; correct interpretation of ambiguous zones: 95%; and movement of pen (temporal order) recovered: 92%.
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paper_title: Recovering dynamic information from static handwriting

paper_content:

It is generally agreed that the advantage of on-line character recognition methods with respect to off-line ones mostly relies on the availability of dynamic information. This mainly concerns the order in which the strokes forming characters have been drawn. In this paper we present and discuss a method which attempts, in the off-line case, to recover part of the lost script dynamics. The method makes it possible to reconstruct one of the most likely trajectories followed by the writer while drawing characters. It is based on a suitable implementation of good continuity criteria which take into account direction, length and width of the strokes making up characters. The algorithm either subdivides or unfolds the digital ribbon forming isolated characters as well as groups of connected characters, by solving the ambiguities which arise at every joint. Experimental results are reported and discussed.
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paper_title: Global methods for stroke segmentation

paper_content:

Two methods for stroke segmentation from a global point of view are presented and compared. One is based on thinning methods and the other is based on contour curve fitting. For both cases an input image is binarized. For the former, Hilditch's method is used, then crossing points are sought, around which a domain is constructed. Outside the domain, a set of line segments are identified. These lines are connected and approximated by cubic B-spline curves. Smoothly connected lines are selected as segmented curves. This method works well for a limited class of crossing lines, which are shown experimentally. In the latter, a contour line is approximated by cubic B-spline curve, along which curvature is measured. According to the extreme points of the curvature graph, the contour line is segmented, based on which the line segment is obtained. Experimental results are shown for some difficult cases.
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paper_title: A psychomotor method for tracking handwriting

paper_content:

The paper proposes a model for tracking handwriting based on curvature minimization. Aiming at recovering stroke sequences from words written in the past, the problem is formulated as a graph theoretical question. The solution does not require mathematical functions. In particular loops are considered elementary basic strokes and are not approximated with functions. Recovering stroke sequences is equivalent to ordering the edges of a graph, which can be derived in a straightforward manner from the scanned binary word image. The paper does not deal with any special recognition method. However, an important aim is to provide a mechanism for deriving temporal information to help to improve off-line recognition methods. The most important advantages of this method over methods proposed so far are: a single global principle (global optimization of curvature), implicit modeling of retraced strokes and simplicity.
---
paper_title: Global and Strict Curve Fitting Method

paper_content:

To find a global and smooth curve fitting, cubic B­Spline method and gathering­ line methods are investigated. When segmenting and recognizing a contour curve of character shape, some global method is required. If we want to connect contour curves around a singular point like crossing points, merging separated contours together which lies apart crossing the singular point is necessary. For this pur pose, cubic B­Spline method and new line­gathering method are investigated and proposed. The result is that cubic B­Spline method is rather too easy to bend. Easy to bend feature can cover singular point smoothly, so is not good to detect singular points. Gathering­line method is to represent the contour by overlapped line segments. By overlapping lines, arcs can be represented in natural way. Some investigations and experimental results are shown.
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paper_title: A Kalman approach for stroke order recovering from off-line handwriting

paper_content:

Non-constrained handwriting recognition is still faced with very difficult problems. However, on-line recognition methods exhibit better results than off-line methods which lose all temporal information. The aim of the work is to recover the strokes ordering from static 2-D images as it is inherently available from on-line systems. The approach is innovative because it uses extensively gray-level information, and uses Kalman filtering in the prediction of the writing stroke trajectory.
---
paper_title: A total stroke SLALOM method for searching for the optimal drawing order of off-line handwriting

paper_content:

Handwriting is easier to recognize when it is done online than when it is done off-line, because for online handwriting one can use the time information obtained during the writing process, whereas for off-line handwriting one must depend solely on the static 2D image, in which the time information has already been lost. We propose in this paper a novel approach called the total stroke SLALOM (TSS) method for recovering the time information from a static 2D image of single-stroke cursive script. To cope with the increased computational burden resulting from the complexity of the script, we divide the image into small components and rely on the principle of "divide and conquer".
---
paper_title: A Framework Toward Restoration of Writing Order from Single-Stroked Handwriting Image

paper_content:

Restoration of writing order from a single-stroked handwriting image can be seen as the problem of finding the smoothest path in its graph representation. In this paper, a 3-phase approach to restore a writing order is proposed within the framework of the edge continuity relation (ECR). In the initial, local phase, in order to obtain possible ECRs at an even-degree node, a neural network is used for the node of degree 4 and a theoretical approach is presented for the node of degree higher than 4 by introducing certain reasonable assumptions. In the second phase, we identify double-traced lines by employing maximum weighted matching. This makes it possible to transform the problem of obtaining possible ECRs at odd-degree node to that at even-degree node. In the final, global phase, we find all the candidates of single-stroked paths by depth first search and select the best one by evaluating SLALOM smoothness. Experiments on static images converted from online data in the Unipen database show that our method achieves a restoration rate of 96.0 percent
---
paper_title: Extraction of strokes in handwritten characters

paper_content:

Abstract Among the many handwritten character recognition algorithms that have been proposed in the past few years, few of them use models which are able to simulate handwriting. This can be explained by the fact that simulation models require the estimation of strokes starting from statistic images of letters, while crossing and overlapping strokes make this estimation difficult. The approach we suggest is to efficiently deal with crossing areas and overlaps using parametric representations of lines and thickness of stroke: a probabilistic model of strokes is described to extract non-overlapping strokes of the image. A bayesian approach using a statistical study and a model of stroke crossing is described that optimizes the reconstruction of crossings and permits to characterize image of letters by robust graphs of curves.
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paper_title: Document image understanding: integrating recovery and interpretation

paper_content:

Many document image understanding problems require a more comprehensive examination of document features than is typically deemed necessary for recognition tasks. We believe that these problems require a detailed analysis of stroke and sub-stroke features in the document image with the goal of obtaining information about the environment or process which created the document and establishing a context for understanding. ::: We introduce the concept of recovery into the document domain. We provide a "stroke platform" representation which establishes a verifiable "link to the pixels" and demonstrate its usefulness for recovery tasks. This representation allows us to overcome many of the problems associated with the rapid, irreversible abstraction associated with traditional document processing methods and provides the basic framework for our analysis of handwritten documents. By obtaining a detailed description of the document and its properties, we are able to establish a context for analysis and validate assumptions about the domain. This dissertation presents our work on several document image understanding problems: (1) demonstrating the successful use of the stroke platform for the problem of interpreting and reconstructing junctions and endpoints, (2) exploring the effects of the handwriting process on the document by the development of a model for instrument grasp and a study of its effects on pressure features, (3) posing and providing an approach to the problem of recovering temporal information from static images of handwriting, (4) addressing various sub-tasks of the problem of processing form documents, and (5) extending the detailed analysis philosophy to demonstrate its feasibility in related document domains.
---
paper_title: Recovery of drawing order from single-stroke handwriting images

paper_content:

Describes a method to recover a drawing order of a handwritten script from a static 2D image. The script should be written in a single stroke and may include double-traced lines. After the script is scanned in and preprocessed, we apply our recovery method which consists of two phases. In the first phase, we globally analyze the graph constructed from the skeletal image and label the graph by determining the types of each edge. In the second phase, we trace the graph from the start vertex to the end vertex using the labeling information. This method does not enumerate the possible cases, for example, by solving the traveling salesman problem and, therefore, does not cause a combinatorial explosion even if the script is very complex. By recovering a drawing order of a handwritten script, the temporal information can be recovered from a static 2D image. Hence, this method will be used as a bridge from the offline handwriting character recognition problem to the online one.
---
paper_title: Hidden loop recovery for handwriting recognition

paper_content:

One significant challenge in the recognition of off-line handwriting is in the interpretation of loop structures. Although this information is readily available in online representation, close proximity of strokes often merges their centers making them difficult to identify. In this paper a novel approach to the recovery of hidden loops in off-line scanned document images is presented. The proposed algorithm seeks blobs that resemble truncated ellipses. We use a sophisticated form analysis method based on mutual distance measurements between the two sides of a symmetric shape. The experimental results are compared with the ground truth of the online representations of each off-line word image. More than 86% percent of the meaningful loops are handled correctly.
---
paper_title: A Framework Toward Restoration of Writing Order from Single-Stroked Handwriting Image

paper_content:

Restoration of writing order from a single-stroked handwriting image can be seen as the problem of finding the smoothest path in its graph representation. In this paper, a 3-phase approach to restore a writing order is proposed within the framework of the edge continuity relation (ECR). In the initial, local phase, in order to obtain possible ECRs at an even-degree node, a neural network is used for the node of degree 4 and a theoretical approach is presented for the node of degree higher than 4 by introducing certain reasonable assumptions. In the second phase, we identify double-traced lines by employing maximum weighted matching. This makes it possible to transform the problem of obtaining possible ECRs at odd-degree node to that at even-degree node. In the final, global phase, we find all the candidates of single-stroked paths by depth first search and select the best one by evaluating SLALOM smoothness. Experiments on static images converted from online data in the Unipen database show that our method achieves a restoration rate of 96.0 percent
---
paper_title: Processing of text documents: straight line approximation and lost loop recovery

paper_content:

This paper deals with two different problems in processing of text documents. Firstly, an integrated algorithm that finds a straight line approximation of a textual stroke is described. It has the advantage of using the distance transform of thinned binary images to identify, spurious bifurcation points which are unavoidable when thinning algorithms are used, remove them, and recover the original ones. Secondly, a method is presented to recover loops that become blobs due to blotting. The method depends on removing the pixels whose distance transform exceeds a calculated threshold.
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paper_title: Offline Loop Investigation for Handwriting Analysis

paper_content:

Resolution of different types of loops in handwritten script presents a difficult task and is an important step in many classic word recognition systems, writer modeling, and signature verification. When processing a handwritten script, a great deal of ambiguity occurs when strokes overlap, merge, or intersect. This paper presents a novel loop modeling and contour-based handwriting analysis that improves loop investigation. We show excellent results on various loop resolution scenarios, including axial loop understanding and collapsed loop recovery. We demonstrate our approach for loop investigation on several realistic data sets of static binary images and compare with the ground truth of the genuine online signal.
---
paper_title: Recovery of drawing order from single-stroke handwriting images

paper_content:

Describes a method to recover a drawing order of a handwritten script from a static 2D image. The script should be written in a single stroke and may include double-traced lines. After the script is scanned in and preprocessed, we apply our recovery method which consists of two phases. In the first phase, we globally analyze the graph constructed from the skeletal image and label the graph by determining the types of each edge. In the second phase, we trace the graph from the start vertex to the end vertex using the labeling information. This method does not enumerate the possible cases, for example, by solving the traveling salesman problem and, therefore, does not cause a combinatorial explosion even if the script is very complex. By recovering a drawing order of a handwritten script, the temporal information can be recovered from a static 2D image. Hence, this method will be used as a bridge from the offline handwriting character recognition problem to the online one.
---
paper_title: Recovery of a drawing order from off-line isolated letters dedicated to on-line recognition

paper_content:

This paper presents a method to generate an equivalent on-line signal of a letter image. To do this, an off-line drawing order has to be recovered using handwriting knowledge. Our approach consists in looking for several start and the end points. Then a reconstruction algorithm is applied and finally the best path is chosen. This approach has been validated on a database of handwritten letters including both on-line and off-line signals. An on-line recognition system has been used to recognize these letters.
---
paper_title: Handwriting Identification: Facts and Fundamentals

paper_content:

Introduction: The World of Documents The History of Questioned Document Examination -In Brief A Handwriting Compendium for Document Examiners The Discrimination of Handwriting The Premises for the Identification of Handwriting The Fundamentals of the Identification Process The Discrimination and Identification of Writing Special Problems in the Discrimination and Identification of Writing The Extrinsical Factors Influencing Handwriting The Intrinsical Influences upon Handwriting The Requirements and the Results The Diagnosis of Writing Identification The Scope of Document Examination The Sources of Document Examiners Science, Scientific Method and Writing Identification Graphology Understanding the Terms-Glossary Epilogue
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paper_title: What Knowledge about Handwritten Letters can be Used to Recover their Drawing Order ?

paper_content:

A way to do off-line handwriting recognition is to generate an equivalent on-line signal of a letter image and to use an on-line recognition system to recognize this letter. To do this, the drawing order has to be recovered using handwriting knowledge. Our approach to recover the drawing order consists in proposing several starting and ending points. Several paths are generated and the best one is chosen. This paper presents the handwriting knowledge introduced at several steps of our method and its contribution for the recognition process. Experimentations have been carried out on isolated lower case multistroke letters from a database including both on-line and off-line signals. An on-line system has been used to recognize these letters.
---
paper_title: Stroke extraction and stroke sequence estimation on signatures

paper_content:

This article addresses the problem of stroke extraction and stroke sequence estimation from a static signature. A new algorithm is proposed based on a simple but effective writer model. The results are encouraging.
---
paper_title: A total stroke SLALOM method for searching for the optimal drawing order of off-line handwriting

paper_content:

Handwriting is easier to recognize when it is done online than when it is done off-line, because for online handwriting one can use the time information obtained during the writing process, whereas for off-line handwriting one must depend solely on the static 2D image, in which the time information has already been lost. We propose in this paper a novel approach called the total stroke SLALOM (TSS) method for recovering the time information from a static 2D image of single-stroke cursive script. To cope with the increased computational burden resulting from the complexity of the script, we divide the image into small components and rely on the principle of "divide and conquer".
---
paper_title: Recovery of temporal information from off-line Arabic handwritten

paper_content:

Summary form only given. In this paper, we present a system of restoration of temporal order in the offline Arabic handwritten tracing. The word image, captured in level of gray from a scanner with a resolution of 300 dpi, passes by four stages of preprocessing: binarization, filtering, smoothing and elimination of the diacritical signs. A first algorithm calculates for every point of the contour a curvature function. The local maximas of this function correspond to the dominant points of the contour. The dominant points correspond to all angular variations on the contour (end of the stroke, cross point, branch point, pocket, buckle...). A sweep of a window of a dimension equal to the triple of the thickness, of the stroke permits to detect cross and branch points of features and a suitable algorithm permits to solve the problem of the temporal order in these ambiguous zones. Finally, another algorithm permits to detect the starting point and to follow the tracing in order to reconstitute the order in which it has been written. The system is improved by an algorithm that treats the particular cases concerning the starting point, the ambiguous zone analysis and the progression of the system at the time of the follow-up of the contour. Preliminary results obtained from our basis (50 words written by 2 people), are: detection of the point of departure: 96%; correct interpretation of ambiguous zones: 95%; and movement of pen (temporal order) recovered: 92%.
---
paper_title: Recovery of drawing order from single-stroke handwriting images

paper_content:

Describes a method to recover a drawing order of a handwritten script from a static 2D image. The script should be written in a single stroke and may include double-traced lines. After the script is scanned in and preprocessed, we apply our recovery method which consists of two phases. In the first phase, we globally analyze the graph constructed from the skeletal image and label the graph by determining the types of each edge. In the second phase, we trace the graph from the start vertex to the end vertex using the labeling information. This method does not enumerate the possible cases, for example, by solving the traveling salesman problem and, therefore, does not cause a combinatorial explosion even if the script is very complex. By recovering a drawing order of a handwritten script, the temporal information can be recovered from a static 2D image. Hence, this method will be used as a bridge from the offline handwriting character recognition problem to the online one.
---
paper_title: Recovery of a drawing order from off-line isolated letters dedicated to on-line recognition

paper_content:

This paper presents a method to generate an equivalent on-line signal of a letter image. To do this, an off-line drawing order has to be recovered using handwriting knowledge. Our approach consists in looking for several start and the end points. Then a reconstruction algorithm is applied and finally the best path is chosen. This approach has been validated on a database of handwritten letters including both on-line and off-line signals. An on-line recognition system has been used to recognize these letters.
---
paper_title: An introduction to hidden Markov models

paper_content:

The basic theory of Markov chains has been known to mathematicians and engineers for close to 80 years, but it is only in the past decade that it has been applied explicitly to problems in speech processing. One of the major reasons why speech models, based on Markov chains, have not been developed until recently was the lack of a method for optimizing the parameters of the Markov model to match observed signal patterns. Such a method was proposed in the late 1960's and was immediately applied to speech processing in several research institutions. Continued refinements in the theory and implementation of Markov modelling techniques have greatly enhanced the method, leading to a wide range of applications of these models. It is the purpose of this tutorial paper to give an introduction to the theory of Markov models, and to illustrate how they have been applied to problems in speech recognition.
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paper_title: Recovery of temporal information of cursively handwritten words for on-line recognition

paper_content:

On-line recognition differs from off-line recognition in that additional information about the drawing order of the strokes is available. This temporal information makes it easier to recognize handwritten texts with an on-line recognition system. In this paper we present a method for the recovery of the stroke order from static handwritten images. The algorithm was tested by classifying the words of an off-line database with a state-of-the-art on-line recognition system. On this database with 150 different words, written by four cooperative writers, a recognition rate of 97.4% was obtained.
---
paper_title: Recovering writing traces in off-line handwriting recognition: using a global optimization technique

paper_content:

Off-line handwriting recognition is constrained by the lack of time information. This paper describes an approach to derive sequence information from a written word. The underlying representation of the word used is a graph with nodes and edges resulting from a skeletonization process. The writing path is reconstructed by assuming that the writer has used a path with least curvature, i.e. reconstruction is realized as a global optimization process, which consists of solving a travelling salesman problem.
---
paper_title: Recovery of drawing order from scanned images of multi-stroke handwriting

paper_content:

Describes a method to recover the drawing order of a multi-stroke handwritten script from a binary 2D image. First, we construct a graph from the scanned image by applying a thinning process and extracting the skeletal pixels. Next, we identify the start or end vertices in the graph, and then globally analyze the graph to label it by determining the types of each vertex and each edge. Finally, we trace all the strokes using the labeling information and recover the drawing order. The method does not enumerate the possible drawing orders and does not cause a combinatorial explosion, even if the script is very complex. By recovering the drawing order of a handwritten script, the temporal information can be recovered from a scanned image. Hence, this method can be used as a bridge from the offline handwriting character recognition problem to the online one.
---
paper_title: Temporal Order Recovery of the Scanned Handwriting

paper_content:

In this paper, we present a new approach to the temporal order restoration of the off-line handwriting. After the pre-processing steps of the word image, a suitable algorithm makes it possible to segment its skeleton in three types of strokes. After that, we developed a genetic algorithm GA in order to optimize the best trajectory of these segments. The repetition of a segment will be studied in a secondary algorithm so that we do not disturb the GA operations. The techniques used in GA are the selection, crossover and the mutation. The fitness function value depends on right-left direction (direction of the Arab writing), the segments repetition and angular deviation on the crossing of the occlusion stroke. To validate our approach, we tested it on the On/Off LMCA dual Arabic handwriting, the Latin IRONOFF and the off-line IFN/ENIT datasets.
---
paper_title: Estimating the pen trajectories of static signatures using hidden Markov models

paper_content:

Static signatures originate as handwritten images on documents and by definition do not contain any dynamic information. This lack of information makes static signature verification systems significantly less reliable than their dynamic counterparts. This study involves extracting dynamic information from static images, specifically the pen trajectory while the signature was created. We assume that a dynamic version of the static image is available (typically obtained during an earlier registration process). We then derive a hidden Markov model from the static image and match it to the dynamic version of the image. This match results in the estimated pen trajectory of the static image.
---
paper_title: Offline Signature Verification System Based on the Online Data

paper_content:

Most of the signature verification work done in the past years focused either on offline or online approaches. In this paper, a different methodology is proposed, where the online reference data acquired through a digitizing tablet serves as the basis for the segmentation process of the corresponding scanned offline data. Local windows are built over the image through a self-adjustable learning process and are used to focus on the feature extraction step. The window's positions are determined according to the complexity of the underlying strokes based on the observation of a delta-lognormal handwritten reproduction model. Local features extraction that takes place focused on the windows formed, and it is used in conjunction with the global primitives to feed the classifier. The overall performance of the system is then measured with three different classification schemes.
---
paper_title: A hybrid on/off line handwritten signature verification system

paper_content:

This paper proposes a new hybrid handwritten signature verification system where the on-line reference data acquired through a digitizing tablet serves as the basis for the segmentation process of the corresponding scanned off-line data. Local foci of attention over the image are determined through a self-adjustable learning process in order to pinpoint the feature extraction process. Both local and global primitives are processed and the decision about the authenticity of the specimen is defined through similarity measurements. The global performance of the system is measured using two different classifiers.
---
paper_title: A tutorial on hidden Markov models and selected applications in speech recognition

paper_content:

This tutorial provides an overview of the basic theory of hidden Markov models (HMMs) as originated by L.E. Baum and T. Petrie (1966) and gives practical details on methods of implementation of the theory along with a description of selected applications of the theory to distinct problems in speech recognition. Results from a number of original sources are combined to provide a single source of acquiring the background required to pursue further this area of research. The author first reviews the theory of discrete Markov chains and shows how the concept of hidden states, where the observation is a probabilistic function of the state, can be used effectively. The theory is illustrated with two simple examples, namely coin-tossing, and the classic balls-in-urns system. Three fundamental problems of HMMs are noted and several practical techniques for solving these problems are given. The various types of HMMs that have been studied, including ergodic as well as left-right models, are described. >
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paper_title: A Window-Based Hybrid Signature Verification System

paper_content:

In this work we present a hybrid handwritten signature verification system where the on-line reference data acquired through a digitizing tablet serves as the basis for the segmentation process of the corresponding scanned off-line data. Local windows are determined over the image through a self-adjustable learning process and are used to focus the feature extraction step. The positions of the windows are determined according to the complexity of the underlying strokes given by the observation of a handwritten reproduction model. Local feature extraction is bounded by the windows formed and it is used with global primitives to feed the classifier. The overall performance of the system is then measured.
---
paper_title: Verification of dynamic curves extracted from static handwritten scripts

paper_content:

Static handwritten scripts originate as images on documents and do not, by definition, contain any dynamic information. To improve the accuracy of static handwriting recognition systems, many techniques aim to estimate dynamic information from the static scripts. Mostly, the pen trajectories of the scripts are estimated. However, the efficacy of the resulting pen trajectories are rarely evaluated quantitatively. This paper proposes a protocol for the objective evaluation of automatically determined pen trajectories. A hidden Markov model is derived from a ground-truth trajectory. An estimated trajectory is then matched to the derived model. Statistics describing substitution, insertion and deletion errors are then computed from this match. The proposed algorithm is especially useful for performance comparisons between different pen trajectory estimation algorithms.
---
paper_title: Estimating the pen trajectories of multi-path static scripts using hidden Markov models

paper_content:

Static handwritten scripts are available only as images on documents and by definition do not contain dynamic information. This study is about extracting dynamic information from a static handwritten script, specifically the sequence of pen positions that created the script. We assume that a dynamic representative of the static image is available (a different version typically obtained during an earlier registration process). A hidden Markov model (HMM) of the static image is compared with the dynamic representative to extract the dynamic information from the static image.
---
paper_title: Recovering dynamic information from static handwriting

paper_content:

It is generally agreed that the advantage of on-line character recognition methods with respect to off-line ones mostly relies on the availability of dynamic information. This mainly concerns the order in which the strokes forming characters have been drawn. In this paper we present and discuss a method which attempts, in the off-line case, to recover part of the lost script dynamics. The method makes it possible to reconstruct one of the most likely trajectories followed by the writer while drawing characters. It is based on a suitable implementation of good continuity criteria which take into account direction, length and width of the strokes making up characters. The algorithm either subdivides or unfolds the digital ribbon forming isolated characters as well as groups of connected characters, by solving the ambiguities which arise at every joint. Experimental results are reported and discussed.
---
paper_title: Recovery of drawing order from single-stroke handwriting images

paper_content:

Describes a method to recover a drawing order of a handwritten script from a static 2D image. The script should be written in a single stroke and may include double-traced lines. After the script is scanned in and preprocessed, we apply our recovery method which consists of two phases. In the first phase, we globally analyze the graph constructed from the skeletal image and label the graph by determining the types of each edge. In the second phase, we trace the graph from the start vertex to the end vertex using the labeling information. This method does not enumerate the possible cases, for example, by solving the traveling salesman problem and, therefore, does not cause a combinatorial explosion even if the script is very complex. By recovering a drawing order of a handwritten script, the temporal information can be recovered from a static 2D image. Hence, this method will be used as a bridge from the offline handwriting character recognition problem to the online one.
---
paper_title: Temporal Order Recovery of the Scanned Handwriting

paper_content:

In this paper, we present a new approach to the temporal order restoration of the off-line handwriting. After the pre-processing steps of the word image, a suitable algorithm makes it possible to segment its skeleton in three types of strokes. After that, we developed a genetic algorithm GA in order to optimize the best trajectory of these segments. The repetition of a segment will be studied in a secondary algorithm so that we do not disturb the GA operations. The techniques used in GA are the selection, crossover and the mutation. The fitness function value depends on right-left direction (direction of the Arab writing), the segments repetition and angular deviation on the crossing of the occlusion stroke. To validate our approach, we tested it on the On/Off LMCA dual Arabic handwriting, the Latin IRONOFF and the off-line IFN/ENIT datasets.
---
paper_title: Ink-deposition model: the relation of writing and ink deposition processes

paper_content:

The paper describes our studies on the influence of physical and biomechanical processes on the ink trace and aims at providing a solid foundation for enhanced signature analysis procedures. By means of a writing robot, simulated human handwriting movements are considered to study the relation between writing process characteristics and ink deposit on paper. Since the robot is able to take up different writing instruments like pencil, ballpoint or fine line pen, the type of inking pen was also varied in the experiments. The results of analyzing these artificial ink traces contribute to a better understanding of the underlying interaction processes and allow for the formulation of a so-called ink-deposition model (IDM). Particularly, we present IDMs that analytically describe the relation of applied pen tip force and relative ink intensity distribution for solid, viscous and fluid ink types. These IDMs might be employed in computer-based analysis of ink trace line quality to recognize skilled forgeries.
---
paper_title: Off-Line Signature Verification Based on Feature Matching

paper_content:

This paper presents a novel approach for off-line signature verification. First the different aspects and problems of signature verification are discussed in conjunction with off-line analysis methods. It is shown, that on-line analysis methods perform usually better than off-line methods because they can make use of the temporal information (and thereby get a better perception of the semantics of the signature). An assumption is made, that off-line verification methods have difficulties improving their results because of the high level of abstraction they use, which is a direct consequence of the missing semantics. Afterwards a solution is presented with the ability to preserve some semantic information, and thereby deliver a better ability to analyze the results and optimize the system.
---
paper_title: A Framework Toward Restoration of Writing Order from Single-Stroked Handwriting Image

paper_content:

Restoration of writing order from a single-stroked handwriting image can be seen as the problem of finding the smoothest path in its graph representation. In this paper, a 3-phase approach to restore a writing order is proposed within the framework of the edge continuity relation (ECR). In the initial, local phase, in order to obtain possible ECRs at an even-degree node, a neural network is used for the node of degree 4 and a theoretical approach is presented for the node of degree higher than 4 by introducing certain reasonable assumptions. In the second phase, we identify double-traced lines by employing maximum weighted matching. This makes it possible to transform the problem of obtaining possible ECRs at odd-degree node to that at even-degree node. In the final, global phase, we find all the candidates of single-stroked paths by depth first search and select the best one by evaluating SLALOM smoothness. Experiments on static images converted from online data in the Unipen database show that our method achieves a restoration rate of 96.0 percent
---
paper_title: Verification of dynamic curves extracted from static handwritten scripts

paper_content:

Static handwritten scripts originate as images on documents and do not, by definition, contain any dynamic information. To improve the accuracy of static handwriting recognition systems, many techniques aim to estimate dynamic information from the static scripts. Mostly, the pen trajectories of the scripts are estimated. However, the efficacy of the resulting pen trajectories are rarely evaluated quantitatively. This paper proposes a protocol for the objective evaluation of automatically determined pen trajectories. A hidden Markov model is derived from a ground-truth trajectory. An estimated trajectory is then matched to the derived model. Statistics describing substitution, insertion and deletion errors are then computed from this match. The proposed algorithm is especially useful for performance comparisons between different pen trajectory estimation algorithms.
---
paper_title: Recovery of drawing order from single-stroke handwriting images

paper_content:

Describes a method to recover a drawing order of a handwritten script from a static 2D image. The script should be written in a single stroke and may include double-traced lines. After the script is scanned in and preprocessed, we apply our recovery method which consists of two phases. In the first phase, we globally analyze the graph constructed from the skeletal image and label the graph by determining the types of each edge. In the second phase, we trace the graph from the start vertex to the end vertex using the labeling information. This method does not enumerate the possible cases, for example, by solving the traveling salesman problem and, therefore, does not cause a combinatorial explosion even if the script is very complex. By recovering a drawing order of a handwritten script, the temporal information can be recovered from a static 2D image. Hence, this method will be used as a bridge from the offline handwriting character recognition problem to the online one.
---
paper_title: What Knowledge about Handwritten Letters can be Used to Recover their Drawing Order ?

paper_content:

A way to do off-line handwriting recognition is to generate an equivalent on-line signal of a letter image and to use an on-line recognition system to recognize this letter. To do this, the drawing order has to be recovered using handwriting knowledge. Our approach to recover the drawing order consists in proposing several starting and ending points. Several paths are generated and the best one is chosen. This paper presents the handwriting knowledge introduced at several steps of our method and its contribution for the recognition process. Experimentations have been carried out on isolated lower case multistroke letters from a database including both on-line and off-line signals. An on-line system has been used to recognize these letters.
---
paper_title: Handwriting trajectory movements controlled by a Beta-elliptic model

paper_content:

This paper studies the dependence betweengeometry and kinematics in handwriting generationMovements. Based on a handwriting generationmodel developed earlier, movements parameters areextracted directly from on-line recording of velocityprofiles. These parameters, which reflect dynamicprocess, are found to be highly related to thegeometry of handwriting movements.The overall approach is based upon thehypothesis that complex human movements can besegmented into, basic and simple units. In otherwords, due to the intrinsic properties of theneuromuscular system involved in a rapid writingtask, there is a class of simple movements, hereaftercalled strokes. More complex movements are thusgenerated by the addition of the various strokesbelonging to such a class.
---
paper_title: Verification of Handwritten Signatures: an Overview

paper_content:

In the information and communication personal verification is a crucial aspect. Among the different means for personal verification, handwritten signature plays a fundamental role since signature is the most diffuse means for personal verification in our daily life. In this paper a brief overview of the field of handwritten signature verification is presented and some of the most relevant perspectives are highlighted.
---
paper_title: Hidden loop recovery for handwriting recognition

paper_content:

One significant challenge in the recognition of off-line handwriting is in the interpretation of loop structures. Although this information is readily available in online representation, close proximity of strokes often merges their centers making them difficult to identify. In this paper a novel approach to the recovery of hidden loops in off-line scanned document images is presented. The proposed algorithm seeks blobs that resemble truncated ellipses. We use a sophisticated form analysis method based on mutual distance measurements between the two sides of a symmetric shape. The experimental results are compared with the ground truth of the online representations of each off-line word image. More than 86% percent of the meaningful loops are handled correctly.
---
paper_title: A Framework Toward Restoration of Writing Order from Single-Stroked Handwriting Image

paper_content:

Restoration of writing order from a single-stroked handwriting image can be seen as the problem of finding the smoothest path in its graph representation. In this paper, a 3-phase approach to restore a writing order is proposed within the framework of the edge continuity relation (ECR). In the initial, local phase, in order to obtain possible ECRs at an even-degree node, a neural network is used for the node of degree 4 and a theoretical approach is presented for the node of degree higher than 4 by introducing certain reasonable assumptions. In the second phase, we identify double-traced lines by employing maximum weighted matching. This makes it possible to transform the problem of obtaining possible ECRs at odd-degree node to that at even-degree node. In the final, global phase, we find all the candidates of single-stroked paths by depth first search and select the best one by evaluating SLALOM smoothness. Experiments on static images converted from online data in the Unipen database show that our method achieves a restoration rate of 96.0 percent
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paper_title: Extracting strokes from static line images based on selective searching

paper_content:

Abstract Extracting the stroke information is important in understanding static line images. An essential issue of stroke extraction by using a thinning process is how to handle intersections where thinning artifacts often occur. In this paper, a novel stroke extraction method based on a “selective searching” technique is proposed. A tree structure is constructed in the vicinity of an intersection so that a correct path of a stroke can be identified by comparing the traveling cost along each candidate path of the tree. The minimum cost path corresponds to the intrinsic trajectory of the stroke at the intersection. A return-cost is calculated for each path so that better performance can be achieved. The new method has been tested extensively with static handwritten numerals extracted from the NIST data base. Experimental results show that the method is effective and reliable.
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paper_title: The Delta LogNormal theory for the generation and modeling of cursive characters

paper_content:

We exploit the Delta LogNormal theory, a powerful tool for the generation and modeling of rapid movements to generate curvilinear strokes and constituting letters that respect both the dynamics and the appearance of movements made by a human. A theoretical analysis of the effects of the various parameters of the model is carried out: first, to reduce the size of the representation space of the letter models; and second, to select the parameters that constitute the optimal conditions for representing various symbols.
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Title: Techniques for Static Handwriting Trajectory Recovery: A Survey

Section 1: INTRODUCTION
Description 1: This section discusses the background and significance of automatic handwriting recognition, including the contrast between on-line and off-line systems and the importance of recovering trajectories to improve recognition performance.

Section 2: MATERIAL
Description 2: This section describes the types of input materials used in trajectory recovery systems, focusing on skeletons and contours of handwriting.

Section 3: PREPROCESSING
Description 3: This section outlines the preprocessing steps necessary for stabilizing the quality of the handwriting static image before further analysis.

Section 4: LOCAL EXAMINATION
Description 4: This section details the local examination processes including the detection and analysis of ambiguous zones, double-traced writing, hidden loops, and endpoint analysis.

Section 5: GLOBAL RECONSTRUCTION
Description 5: This section explains the techniques involved in putting every recovered segment and stroke back into the correct writing order for a reconstructed global trajectory.

Section 6: PERFORMANCE EVALUATION
Description 6: This section covers the methods and metrics used to evaluate the performance of trajectory recovery systems.

Section 7: FINAL REMARKS
Description 7: This section provides a summary of findings, discusses the implications of the survey, and suggests directions for future research.