A Review of Evaluation of Optimal Binarization Technique for Character Segmentation in Historical Manuscripts
---
paper_title: Survey over image thresholding techniques and quantitative performance evaluation

paper_content:

We conduct an exhaustive survey of image thresholding methods, categorize them, express their formulas under a uniform notation, and finally carry their performance comparison. The thresholding methods are categorized according to the information they are exploiting, such as histogram shape, measurement space clustering, entropy, object attributes, spatial correlation, and local gray-level surface. 40 selected thresholding methods from various categories are compared in the context of nondestructive testing applications as well as for document images. The comparison is based on the combined performance measures. We identify the thresholding algorithms that perform uniformly better over nonde- structive testing and document image applications. © 2004 SPIE and IS&T. (DOI: 10.1117/1.1631316)
---
paper_title: Selection of thresholding methods for nondestructive testing applications

paper_content:

In nondestructive testing (NDT) applications based on image analysis, image segmentation is the most important step in the extraction of defective regions of materials. An effective and very simple method of image segmentation, especially in NDT applications, is image thresholding. In an effort to present a quantitative evaluation of image thresholding methods NDT applications, 41 thresholding algorithms are compared. They are grouped in six categories based on the information they are exploiting, such as histogram shape, object attribute or clustering behavior, etc. Performance assessment is based on the weighted combination of four complementary objective metrics. Based on the results of such NDT images as defective thermal, ultrasonic, eddy current, etc., the thresholding algorithms that perform well over the majority of cases are established and a mixture thresholding scheme is proposed.
---
paper_title: An introduction to digital image processing

paper_content:

A new and distinct spur type apple variety which originated as a limb mutation of the standard winter banana apple tree (non-patented) is provided. This new apple variety possesses a vigorous compact and only slightly spreading growth habit and can be distinguished from its parent and the Housden spur type winter banana apple variety (non-patented). More specifically, the new variety forms more fruiting spurs per unit length on two and three year old wood than the standard winter banana apple tree and less spurs per unit length than the Housden spur type winter banana apple tree. Additionally, the new variety has the ability to heavily bear fruit having a whitish-yellow skin color with a sometimes slight scarlet red blush upon maturity which is substantially identical to that of the standard winter banana apple tree and which has substantially less skin russeting than the Housden spur type winter banana apple tree.
---
paper_title: Decompose algorithm for thresholding degraded historical document images

paper_content:

Numerous techniques have previously been proposed for single-stage thresholding of document images to separate the written or printed information from the background. Although these global or local thresholding techniques have proven effective on particular subclasses of documents, none is able to produce consistently good results on the wide range of document image qualities that exist in general or the image qualities encountered in degraded historical documents. A new thresholding structure called the decompose algorithm is proposed and compared against some existing single-stage algorithms. The decompose algorithm uses local feature vectors to analyse and find the best approach to threshold a local area. Instead of employing a single thresholding algorithm, automatic selection of an appropriate algorithm for specific types of subregions of the document is performed. The original image is recursively broken down into subregions using quad-tree decomposition until a suitable thresholding method can be applied to each subregion. The algorithm has been trained using 300 historical images obtained from the Library of Congress and evaluated on 300 ‘difficult’ document images, also extracted from the Library of Congress, in which considerable background noise or variation in contrast and illumination exists. Quantitative analysis of the results by measuring text recall, and qualitative assessment of processed document image quality is reported. The decompose algorithm is demonstrated to be effective at resolving the problem in varying quality historical images.
---
paper_title: A comparison of binarization methods for historical archive documents

paper_content:

This paper compares several alternative binarization algorithms for historical archive documents, by evaluating their effect on end-to-end word recognition performance in a complete archive document recognition system utilising a commercial OCR engine. The algorithms evaluated are: global thresholding; Niblack's and Sauvola's algorithms; adaptive versions of Niblack's and Sauvola's algorithms; and Niblack's and Sauvola's algorithms applied to background removed images. We found that, for our archive documents, Niblack's algorithm can achieve better performance than Sauvola's (which has been claimed as an evolution of Niblack's algorithm), and that it also achieved better performance than the internal binarization provided as part of the commercial OCR engine.
---
paper_title: Goal-Directed Evaluation of Binarization Methods

paper_content:

This paper presents a methodology for evaluation of low-level image analysis methods, using binarization (two-level thresholding) as an example. Binarization of scanned gray scale images is the first step in most document image analysis systems. Selection of an appropriate binarization method for an input image domain is a difficult problem. Typically, a human expert evaluates the binarized images according to his/her visual criteria. However, to conduct an objective evaluation, one needs to investigate how well the subsequent image analysis steps will perform on the binarized image. We call this approach goal-directed evaluation, and it can be used to evaluate other low-level image processing methods as well. Our evaluation of binarization methods is in the context of digit recognition, so we define the performance of the character recognition module as the objective measure. Eleven different locally adaptive binarization methods were evaluated, and Niblack's method gave the best performance.
---
paper_title: Comparing background elimination approaches for processing of ancient Thai manuscipts on palm leaves

paper_content:

The objective of the Preservation of Palm Leaf Manuscripts Project at Mahasarakham University at Thailand is to preserve and retrieve traditional knowledge from ancient manuscripts recorded on palm leaves. An essential task in the process is to recognize the ancient characters automatically through image processing techniques. The paper compares different background elimination approaches which could be used. The aim is to improve the global and local adaptive thresholding techniques intelligently, and to form the pre-processing procedure in the automated process.
---
paper_title: Survey over image thresholding techniques and quantitative performance evaluation

paper_content:

We conduct an exhaustive survey of image thresholding methods, categorize them, express their formulas under a uniform notation, and finally carry their performance comparison. The thresholding methods are categorized according to the information they are exploiting, such as histogram shape, measurement space clustering, entropy, object attributes, spatial correlation, and local gray-level surface. 40 selected thresholding methods from various categories are compared in the context of nondestructive testing applications as well as for document images. The comparison is based on the combined performance measures. We identify the thresholding algorithms that perform uniformly better over nonde- structive testing and document image applications. © 2004 SPIE and IS&T. (DOI: 10.1117/1.1631316)
---
paper_title: An introduction to digital image processing

paper_content:

A new and distinct spur type apple variety which originated as a limb mutation of the standard winter banana apple tree (non-patented) is provided. This new apple variety possesses a vigorous compact and only slightly spreading growth habit and can be distinguished from its parent and the Housden spur type winter banana apple variety (non-patented). More specifically, the new variety forms more fruiting spurs per unit length on two and three year old wood than the standard winter banana apple tree and less spurs per unit length than the Housden spur type winter banana apple tree. Additionally, the new variety has the ability to heavily bear fruit having a whitish-yellow skin color with a sometimes slight scarlet red blush upon maturity which is substantially identical to that of the standard winter banana apple tree and which has substantially less skin russeting than the Housden spur type winter banana apple tree.
---
paper_title: Minimum error thresholding

paper_content:

Abstract A computationally efficient solution to the problem of minimum error thresholding is derived under the assumption of object and pixel grey level values being normally distributed. The method is applicable in multithreshold selection.
---
paper_title: A new method for gray-level picture thresholding using the entropy of the histogram

paper_content:

Two methods of entropic thresholding proposed by Pun (Signal Process.,2, 1980, 223–237;Comput. Graphics Image Process.16, 1981, 210–239) have been carefully and critically examined. A new method with a sound theoretical foundation is proposed. Examples are given on a number of real and artifically generated histograms.
---
paper_title: Histogram concavity analysis as an aid in threshold selection

paper_content:

A well-known heuristic for segmenting an image into gray level subpopulations is to select thresholds at the bottoms of valleys on the image's histogram. When the subpopulations overlap, valleys may not exist, but it is often still possible to define good thresholds at the `shoulders' of histogram peaks. Both valleys and shoulders correspond to concavities on the histogram, and this suggests that it should be possible to find good candidate thresholds by analyzing the histogram's concavity structure. Histogram concavity analysis as an approach to threshold selection is investigated and its performance on a set of histograms of infrared images of tanks is illustrated.
---
paper_title: Goal-Directed Evaluation of Binarization Methods

paper_content:

This paper presents a methodology for evaluation of low-level image analysis methods, using binarization (two-level thresholding) as an example. Binarization of scanned gray scale images is the first step in most document image analysis systems. Selection of an appropriate binarization method for an input image domain is a difficult problem. Typically, a human expert evaluates the binarized images according to his/her visual criteria. However, to conduct an objective evaluation, one needs to investigate how well the subsequent image analysis steps will perform on the binarized image. We call this approach goal-directed evaluation, and it can be used to evaluate other low-level image processing methods as well. Our evaluation of binarization methods is in the context of digit recognition, so we define the performance of the character recognition module as the objective measure. Eleven different locally adaptive binarization methods were evaluated, and Niblack's method gave the best performance.
---
paper_title: Comparing background elimination approaches for processing of ancient Thai manuscipts on palm leaves

paper_content:

The objective of the Preservation of Palm Leaf Manuscripts Project at Mahasarakham University at Thailand is to preserve and retrieve traditional knowledge from ancient manuscripts recorded on palm leaves. An essential task in the process is to recognize the ancient characters automatically through image processing techniques. The paper compares different background elimination approaches which could be used. The aim is to improve the global and local adaptive thresholding techniques intelligently, and to form the pre-processing procedure in the automated process.
---
paper_title: Survey over image thresholding techniques and quantitative performance evaluation

paper_content:

We conduct an exhaustive survey of image thresholding methods, categorize them, express their formulas under a uniform notation, and finally carry their performance comparison. The thresholding methods are categorized according to the information they are exploiting, such as histogram shape, measurement space clustering, entropy, object attributes, spatial correlation, and local gray-level surface. 40 selected thresholding methods from various categories are compared in the context of nondestructive testing applications as well as for document images. The comparison is based on the combined performance measures. We identify the thresholding algorithms that perform uniformly better over nonde- structive testing and document image applications. © 2004 SPIE and IS&T. (DOI: 10.1117/1.1631316)
---
paper_title: A comparison of binarization methods for historical archive documents

paper_content:

This paper compares several alternative binarization algorithms for historical archive documents, by evaluating their effect on end-to-end word recognition performance in a complete archive document recognition system utilising a commercial OCR engine. The algorithms evaluated are: global thresholding; Niblack's and Sauvola's algorithms; adaptive versions of Niblack's and Sauvola's algorithms; and Niblack's and Sauvola's algorithms applied to background removed images. We found that, for our archive documents, Niblack's algorithm can achieve better performance than Sauvola's (which has been claimed as an evolution of Niblack's algorithm), and that it also achieved better performance than the internal binarization provided as part of the commercial OCR engine.
---
paper_title: Comparing background elimination approaches for processing of ancient Thai manuscipts on palm leaves

paper_content:

The objective of the Preservation of Palm Leaf Manuscripts Project at Mahasarakham University at Thailand is to preserve and retrieve traditional knowledge from ancient manuscripts recorded on palm leaves. An essential task in the process is to recognize the ancient characters automatically through image processing techniques. The paper compares different background elimination approaches which could be used. The aim is to improve the global and local adaptive thresholding techniques intelligently, and to form the pre-processing procedure in the automated process.
---
paper_title: Survey over image thresholding techniques and quantitative performance evaluation

paper_content:

We conduct an exhaustive survey of image thresholding methods, categorize them, express their formulas under a uniform notation, and finally carry their performance comparison. The thresholding methods are categorized according to the information they are exploiting, such as histogram shape, measurement space clustering, entropy, object attributes, spatial correlation, and local gray-level surface. 40 selected thresholding methods from various categories are compared in the context of nondestructive testing applications as well as for document images. The comparison is based on the combined performance measures. We identify the thresholding algorithms that perform uniformly better over nonde- structive testing and document image applications. © 2004 SPIE and IS&T. (DOI: 10.1117/1.1631316)
---
paper_title: Selection of thresholding methods for nondestructive testing applications

paper_content:

In nondestructive testing (NDT) applications based on image analysis, image segmentation is the most important step in the extraction of defective regions of materials. An effective and very simple method of image segmentation, especially in NDT applications, is image thresholding. In an effort to present a quantitative evaluation of image thresholding methods NDT applications, 41 thresholding algorithms are compared. They are grouped in six categories based on the information they are exploiting, such as histogram shape, object attribute or clustering behavior, etc. Performance assessment is based on the weighted combination of four complementary objective metrics. Based on the results of such NDT images as defective thermal, ultrasonic, eddy current, etc., the thresholding algorithms that perform well over the majority of cases are established and a mixture thresholding scheme is proposed.
---
paper_title: Optimal combination of document binarization techniques using a self-organizing map neural network

paper_content:

This paper proposes an integrated system for the binarization of normal and degraded printed documents for the purpose of visualization and recognition of text characters. In degraded documents, where considerable background noise or variation in contrast and illumination exists, there are many pixels that cannot be easily classified as foreground or background pixels. For this reason, it is necessary to perform document binarization by combining and taking into account the results of a set of binarization techniques, especially for document pixels that have high vagueness. The proposed binarization technique takes advantage of the benefits of a set of selected binarization algorithms by combining their results using a Kohonen self-organizing map neural network. Specifically, in the first stage the best parameter values for each independent binarization technique are estimated. In the second stage and in order to take advantage of the binarization information given by the independent techniques, the neural network is fed by the binarization results obtained by those techniques using their estimated best parameter values. This procedure is adaptive because the estimation of the best parameter values depends on the content of images. The proposed binarization technique is extensively tested with a variety of degraded document images. Several experimental and comparative results, exhibiting the performance of the proposed technique, are presented.
---
paper_title: Minimum error thresholding

paper_content:

Abstract A computationally efficient solution to the problem of minimum error thresholding is derived under the assumption of object and pixel grey level values being normally distributed. The method is applicable in multithreshold selection.
---
paper_title: An evaluation survey of binarization algorithms on historical documents

paper_content:

Document binarization is an active research area for many years. There are many difficulties associated with satisfactory binarization of document images and especially in cases of degraded historical documents. In this paper, we try to answer the question ldquohow well an existing binarization algorithm can binarize a degraded document image?rdquo We propose a new technique for the validation of document binarization algorithms. Our method is simple in its implementation and can be performed on any binarization algorithm since it doesnpsilat require anything more than the binarization stage. Then we apply the proposed technique to 30 existing binarization algorithms. Experimental results and conclusions are presented.
---
paper_title: Histogram concavity analysis as an aid in threshold selection

paper_content:

A well-known heuristic for segmenting an image into gray level subpopulations is to select thresholds at the bottoms of valleys on the image's histogram. When the subpopulations overlap, valleys may not exist, but it is often still possible to define good thresholds at the `shoulders' of histogram peaks. Both valleys and shoulders correspond to concavities on the histogram, and this suggests that it should be possible to find good candidate thresholds by analyzing the histogram's concavity structure. Histogram concavity analysis as an approach to threshold selection is investigated and its performance on a set of histograms of infrared images of tanks is illustrated.
---
paper_title: A Method for Objective Edge Detection Evaluation and Detector Parameter Selection

paper_content:

Subjective evaluation by human observers is usually used to analyze and select an edge detector parametric setup when real-world images are considered. We propose a statistical objective performance analysis and detector parameter selection, using detection results produced by different detector parameters. Using the correspondence between the different detection results, an estimated best edge map, utilized as an estimated ground truth (EGT), is obtained. This is done using both a receiver operating characteristics (ROC) analysis and a Chi-square test, and considers the trade off between information and noisiness in the detection results. The best edge detector parameter set (PS) is then selected by the same statistical approach, using the EGT. Results are demonstrated for several edge detection techniques, and compared to published subjective evaluation results. The method developed here suggests a general tool to assist in practical implementations of parametric edge detectors where an automatic process is required.
---
paper_title: An evaluation survey of binarization algorithms on historical documents

paper_content:

Document binarization is an active research area for many years. There are many difficulties associated with satisfactory binarization of document images and especially in cases of degraded historical documents. In this paper, we try to answer the question ldquohow well an existing binarization algorithm can binarize a degraded document image?rdquo We propose a new technique for the validation of document binarization algorithms. Our method is simple in its implementation and can be performed on any binarization algorithm since it doesnpsilat require anything more than the binarization stage. Then we apply the proposed technique to 30 existing binarization algorithms. Experimental results and conclusions are presented.
---
paper_title: Comparing background elimination approaches for processing of ancient Thai manuscipts on palm leaves

paper_content:

The objective of the Preservation of Palm Leaf Manuscripts Project at Mahasarakham University at Thailand is to preserve and retrieve traditional knowledge from ancient manuscripts recorded on palm leaves. An essential task in the process is to recognize the ancient characters automatically through image processing techniques. The paper compares different background elimination approaches which could be used. The aim is to improve the global and local adaptive thresholding techniques intelligently, and to form the pre-processing procedure in the automated process.
---

Title: A Review of Evaluation of Optimal Binarization Technique for Character Segmentation in Historical Manuscripts

Section 1: INTRODUCTION
Description 1: This section should introduce the topic of the paper, provide context about the historical manuscripts in Thailand, and explain the importance and challenges of binarization for character segmentation in such documents.

Section 2: BINARIZATION TECHNIQUES
Description 2: This section should describe various binarization techniques, including global and local thresholding methods, and their applications in document image processing.

Section 3: MEASUREMENTS OF IMAGE QUALITY
Description 3: This section should explain different methods and criteria used to evaluate the quality of binarized images, such as misclassification error, edge mismatch, and shape distortion penalty.

Section 4: A FRAMEWORK OF AN AUTOMATIC SELECTION OF OPTIMAL BINARIZATION ALGORITHM
Description 4: This section should present a proposed framework for automatically selecting the best binarization algorithm for historical documents, incorporating steps like algorithm selection, image clustering, feature extraction, and machine learning classification.

Section 5: CONCLUSION AND DISCUSSION
Description 5: This section should summarize the findings of the survey, discuss the limitations of existing binarization techniques, and propose future research directions to improve the automatic selection of binarization algorithms for historical manuscripts.