Handwritten Character Recognition In Malayalam Scripts- A Review
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paper_title: CHAIN CODE AND HOLISTIC FEATURES BASED OCR SYSTEM FOR PRINTED DEVANAGARI SCRIPT USING ANN AND SVM

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Optical Character Recognition Systems are getting more and more attention in recent decade. In many countries, OCR has been a part of their government sectors like post offices, Library automation, License Plate Recognition, Defence organization etc. According to recent survey, there are at least 550 million people are using Devanagari script for communication. Hindi is one of the language, which is derived from Devanagari script. For any character recognition system, essential step is to identify individual character and find features to compare it with the template features. In this paper, we have proposed histogram based hierarchical approach for isolating individual character from the image document. We have used Principle Component Analysis and Fisher Discriminant Analysis kind of holistic features for recognition. We have done the comparisons of such holistic features with geometric features like binary features and chain code.
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paper_title: An introduction to digital image processing

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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.
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paper_title: Thinning methodologies−a comprehensive survey

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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: Skew angle detection of digitized Indian script documents

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Skew angle detection of scanned documents containing most popular Indian scripts (Devnagari and Bangla) is considered. Most characters in these scripts have horizontal lines at the top, called head lines. The character head lines mostly join one another in a word and the word appears as a single component. In the proposed method the components are at first labeled. The upper envelope of a component is found by columnwise scanning from an imaginary line above the component. Portions of upper envelope satisfying the properties of digital straight line are detected. They are clustered as belonging to single text lines. Estimates from individual clusters are combined to get the skew angle. Apart from accuracy and efficiency, an advantage of the method is that character segmentation and zone detection can be readily done from head line information, which is useful in optical character recognition approaches of these scripts.
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paper_title: Feature extraction methods for character recognition-A survey

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This paper presents an overview of feature extraction methods for off-line recognition of segmented (isolated) characters. Selection of a feature extraction method is probably the single most important factor in achieving high recognition performance in character recognition systems. Different feature extraction methods are designed for different representations of the characters, such as solid binary characters, character contours, skeletons (thinned characters) or gray-level subimages of each individual character. The feature extraction methods are discussed in terms of invariance properties, reconstructability and expected distortions and variability of the characters. The problem of choosing the appropriate feature extraction method for a given application is also discussed. When a few promising feature extraction methods have been identified, they need to be evaluated experimentally to find the best method for the given application.
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paper_title: Wavelet Transform and Projection Profiles in Handwritten Character Recognition - A Performance Analysis

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Wavelet transform of projection profile of character images has been found to be suitable for machine recognition of handwritten characters. In this work, performance analysis of such a feature using twelve different wavelet filters and different training / test data sets is carried out. A total of twelve thousand eight hundred handwritten isolated Malayalam characters belonging to 33 classes were used for the study. An MLP network is used as classifier. It is observed that the performance of different wavelet filters used in the study is more or less same. The average recognition accuracy is 76.8%. The above work is extended by adding one more feature, the aspect ratio and found significant improvement in recognition, the average being 81.3%. Considering the relatively large data set used, the result obtained is promising.
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paper_title: Isolated Handwritten Malayalam Character Recognition Using HLH Intensity Patterns

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Recently Indian Handwritten character recognition is getting much more attention and researchers are contributing a lot in this field. But Malayalam, a South Indian language has very less works in this area and needs further attention. This paper focuses on an efficient algorithm for recognizing the handwritten Malayalam characters. Malayalam OCR is a complex task owing to the various character scripts available and more importantly the difference in ways in which the characters are written. The dimensions are never the same and may be never mapped on to a square grid unlike English characters. Here we propose an algorithm which can accept the scanned image of handwritten characters as input and to produce the editable Malayalam characters in a predefined format as output without applying any resizing or skeletonization methods but still can produce much accurate results. Characters are grouped in to different classes based on their HLH intensity patterns. These patterns are separated from the image and fed for recognition. Algorithm is tested for 4 sets of samples ranging 661 letters in the noiseless environment and produces an accuracy of 88%.
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paper_title: Handwritten Character Recognition Using Gray-scale Based State-Space Parameters and Class Modular NN

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We present a novel feature extraction method for offline recognition of segmented Malayalam handwritten characters from their gray-scale images without the usual step of binarization. We investigate a new approach to model handwritten characters using state-space map (SSM) and state-space point distribution (SSPD) parameters. In the recognition stage we used class modular neural network with the proposed SSPD features and this method is found to be promising.
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paper_title: Handwritten character recognition using wavelet energy and extreme learning machine

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This paper deals with the recognition of handwritten Malayalam character using wavelet energy feature (WEF) and extreme learning machine (ELM). The wavelet energy (WE) is a new and robust parameter, and is derived using wavelet transform. It can reduce the influences of different types of noise at different levels. WEF can reflect the WE distribution of characters in several directions at different scales. To a non oscillating pattern, the amplitudes of wavelet coefficients increase when the scale of wavelet decomposition increase. WE of different decomposition levels have different powers to discriminate the character images. These features constitute patterns of handwritten characters for classification. The traditional learning algorithms of the different classifiers are far slower than required. So we have used an extremely fast leaning algorithm called ELM for single hidden layer feed forward networks (SLFN), which randomly chooses the input weights and analytically determines the output weights of SLFN. This algorithm learns much faster than traditional popular learning algorithms for feed forward neural networks. This feature vector, classifier combination gave good recognition accuracy at level 6 of the wavelet decomposition.
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paper_title: Offline handwritten Malayalam Character Recognition based on chain code histogram

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Optical Character Recognition plays an important role in Digital Image Processing and Pattern Recognition. Even though ambient study had been performed on foreign languages like Chinese and Japanese, effort on Indian script is still immature. OCR in Malayalam language is more complex as it is enriched with largest number of characters among all Indian languages. The challenge of recognition of characters is even high in handwritten domain, due to the varying writing style of each individual. In this paper we propose a system for recognition of offline handwritten Malayalam vowels. The proposed method uses Chain code and Image Centroid for the purpose of extracting features and a two layer feed forward network with scaled conjugate gradient for classification.
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paper_title: Modified quadratic classifier for Handwritten Malayalam Character recognition using Run Length Count

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The strength of the selected feature and the effectiveness of the classifier are the two key factors determining the performance of a handwritten Character Recognition System. In this work, we implemented a feature extraction method based on Run Length Count (RLC) for the offline recognition of Handwritten Malayalam Characters. RLC is the count of contiguous group of 1's encountered in a left to right / top to bottom scan of a character image or block of an image. Fixed Meshing strategy is followed for blocking the character images and RLC of the different blocks forms the feature vector for classification. For classification, we implemented Modified Quadratic Discriminant function (MQDF), which is a successful statistical approach for Handwritten Character Recognition. The classifier gives 94.18% accuracy for a feature vector of size 51, which is a significant achievement in isolated Malayalam HCR systems. The study was carried out with a database containing 15,000 handwritten Malayalam character samples. Feature extraction with RLC contributes to the work by its simplicity and lesser execution time. Compared to neural network, MQDF require much lesser training time.
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paper_title: Content based image retrieval system for Malayalam handwritten characters

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Content Based Image Retrieval is one of the prominent areas in Computer Vision and Image Processing. Recognition of handwritten characters has been a popular area of research for many years and still remains an open problem. The proposed system uses visual image queries for retrieving similar images from database of Malayalam handwritten characters. Local Binary Pattern (LBP) descriptors of the query images are extracted and those features are compared with the features of the images in database for retrieving desired characters. This system with local binary pattern gives excellent retrieval performance.
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paper_title: Recognition of Unconstrained Handwritten Malayalam Characters Using Zero-crossing of Wavelet Coefficients

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This work focuses on application of Wavelets in offline recognition of unconstrained isolated handwritten Malayalam characters. The data set consists of 30 samples of each 25 consonants (out of 36) in Malayalam (one of the South Indian Languages). All samples are 256 x 256 gray level images. No preprocessing (such as denoising and thinning) is performed. The images are converted to inverted binary images and wavelet transform is applied (using Db4 filter). For each image, count of zero-crossing in each of the ten subbands is found and is used as the feature for classification. From the analysis of the range of zero-crossing values in different subbands, the 25 characters could be classified into 11 sets. The result from this preliminary work is promising. Hence a detailed study on effect of preprocessing, use of different filters and application of Neuro-Fuzzy classifier is under investigation.
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paper_title: Recognition of handwritten Malayalam characters using vertical & horizontal line positional analyzer algorithm

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This paper proposes an algorithm for the recognition of handwritten characters in Malayalam, a South Indian language. It introduces the salient features of Malayalam script and lists the approaches used for character recognition. Malayalam scripts are rich in patterns because of their complex curved form, larger number of basic elements and the presence of conjuncts. The combinations of such patterns make the recognition of characters much complex and these patterns should be exploited to arrive at the solution. Here an image of handwritten Malayalam characters is given as the input and an editable document of Malayalam characters in a predefined format is produced as output. In this paper, initially the overall structure of OCR system is presented. Then, the OCR process is presented in three modules: Pre-processing, Skeletonization and Recognition. In Pre-processing, we scan the input image and separate each character from it. In Skeletoniz ation, we obtain one pixel thick skeleton of the character. In Recognition, we classify the characters based on their features. The features of the characters are extracted based on the analysis of position and count of the horizontal and vertical lines.
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paper_title: Unconstrained Handwritten Malayalam Character Recognition using Wavelet Transform and Support vector Machine Classifier

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Abstract This paper presents the application of wavelet processing in the domain of handwritten character recognition. To attain high recognition rate, robust feature extractors and powerful classifiers that are invariant to degree of variability of human writing are needed. The proposed scheme consists of two stages: a feature extraction stage, which is based on Haar wavelet transform and a classification stage that uses support vector machine classifier. Experimental results show that the proposed method is effective.
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paper_title: Online handwritten character recognition for Malayalam

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Handwritten character recognition is conversion of handwritten text to machine readable and editable form. Online character recognition deals with live conversion of characters. Malayalam is a language spoken by millions of people in the state of Kerala and the union territories of Lakshadweep and Pondicherry. It is written mostly in clockwise direction and consists of loops and curves. It is possible that the characters may get disrupted during writing due to several reasons such as fast writings, low pen pressure, etc. Hence, it becomes important to include linking of broken characters as an important task in process of character recognition. This paper proposes an online method for identification of Malayalam handwritten characters and also deals with linking disrupted characters, if any.
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paper_title: k-NN Based On-Line Handwritten Character Recognition System

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On-line handwriting recognition has been a frontier area of research for the last few decades under the purview of pattern recognition. Word processing turns to be a vexing experience even if it is with the assistance of an alphanumeric keyboard in Indian languages. A natural solution for this problem is offered through online character recognition. There is abundant literature on the handwriting recognition of western, Chinese and Japanese scripts, but there are very few related to the recognition of Indic script such as Malayalam. This paper presents an efficient Online Handwritten character Recognition System for Malayalam Characters (OHR-M) using K-NN algorithm. It would help in recognizing Malayalam text entered using pen-like devices. A novel feature extraction method, a combination of time domain features and dynamic representation of writing direction along with its curvature is used for recognizing Malayalam characters. This writer independent system gives an excellent accuracy of 98.125% with recognition time of 15-30 milliseconds.
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paper_title: Malayalam online handwriting recognition system: A simplified fuzzy ARTMAP approach

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On-line handwriting recognition is the automatic conversion of handwritten text into letter codes as it is written on a special digitizer or PDA which is usable within computer and text-processing applications. Some of the commonly used techniques for online handwritten characters include Hidden Markov Model (HMM), elastic matching, structural analysis, Time delay neural networks, and combination of multiple classifiers. This paper describes Malayalam online handwriting recognition system using SFAM artificial neural network technique. The structural and directional information is extracted/collected from each character/stroke. The extracted features, which form the feature vector, are passed as input to a Simplified Fuzzy ARTMAP (SFAM) artificial neural network (ANN) classifier. The SFAM classifier compares the input data with the trained data and finds the nearest prototype from the database that ‘resonates’ with the input pattern. The labels or recognized characters are assigned their corresponding Unicode code points and displayed using appropriate fonts. Using this technique we have obtained an average accuracy of 98.26%.
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paper_title: On-line Malayalam handwritten character recognition using HMM and SVM

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Nowadays HMMs are widely used for the online recognition of handwriting characters in different languages, and they are dominating as one of the standard choices for time series classification among research communities. Disadvantages of the systems using HMM are large training time as well as higher computational cost especially at higher number of states. Support Vector Machines is another statistical method based on the structural risk minimization principle and is capable of dealing with features of higher dimensionality. This paper presents the comparative performance of On-line Malayalam handwritten character recognition system using HMM and SVM. Time domain features such as input co-ordinates and the angular features are basically used to form the feature vector. A novel feature extraction method based on discrete wavelet transform is used for the system using SVM. The system gives maximum accuracy of 97.97% for SVM and 95.24% for HMM when tested on 1279 character samples, also the system using SVM require very much less training time compared to those using HMM.
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paper_title: Online sequential extreme learning machine based handwritten character recognition

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The machine recognition of handwriting has been studied several decades, and still is an active research area due to the presence of many challenging issues. Several measures have been taken to improve the recognition accuracy and decrease computational burden and time involved in the overall recognition process. This work is carried out in this context, and tried to achieve the above goals. A discriminative feature set is an important component in any pattern recognition system. We have used division point features generated by recursive subdivision of chamaracter images. These features are extracted from a size normalized binary image. In many of the applications data arrive chunk by chunk or one by one, and we need a classifier which handles the data sequentially. In this paper, a fast and accurate online sequential algorithm known as OS-ELM is used for single hidden layer feed forward neural networks (SLFN) with both additive and radial basis function (RBF) hidden nodes in a unified way. We have also considered the fact that the complexity of the system increases as the number of hidden layers increases. An SLFN trained with OS-ELM is used for the classification of 44 class handwritten Malayalam characters. The only control parameter required for this system is the size of the hidden layer. After several practices we have achieved a recognition accuracy of 96.83% with 1200 neurons in the hidden layer.
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paper_title: On-line Malayalam handwritten character recognition using wavelet transform and SFAM

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The aim of this paper is to present a new practically implementable method, based on wavelet transform, for the on-line recognition of Malayalam handwritten characters. Wavelet transform of the input coordinates and the angle features is used to form the feature vector in compressed form. The classifier used is Simplified Fuzzy ARTMAP network, which requires comparatively very less time for training also supports incremental learning making it ideal for practical implementation. We have analyzed the recognition accuracy of the system using ‘dbl’ and ‘haar’ wavelets. The system, when tested on 1279 character samples gives a maximum accuracy of 97.81%. The training time required is less than five minuites and the recognition time is about 0.5sec/symbol.
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paper_title: Malayalam online handwriting recognition system: A simplified fuzzy ARTMAP approach

paper_content:

On-line handwriting recognition is the automatic conversion of handwritten text into letter codes as it is written on a special digitizer or PDA which is usable within computer and text-processing applications. Some of the commonly used techniques for online handwritten characters include Hidden Markov Model (HMM), elastic matching, structural analysis, Time delay neural networks, and combination of multiple classifiers. This paper describes Malayalam online handwriting recognition system using SFAM artificial neural network technique. The structural and directional information is extracted/collected from each character/stroke. The extracted features, which form the feature vector, are passed as input to a Simplified Fuzzy ARTMAP (SFAM) artificial neural network (ANN) classifier. The SFAM classifier compares the input data with the trained data and finds the nearest prototype from the database that ‘resonates’ with the input pattern. The labels or recognized characters are assigned their corresponding Unicode code points and displayed using appropriate fonts. Using this technique we have obtained an average accuracy of 98.26%.
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Title: Handwritten Character Recognition In Malayalam Scripts- A Review

Section 1: INTRODUCTION
Description 1: This section introduces the topic of handwritten character recognition, its importance, the difference between online and offline recognition, challenges in recognizing South Indian scripts, and the organization of the paper.

Section 2: Components Of A General Character Recognition System
Description 2: This section explains the major steps involved in a handwritten character recognition system including data acquisition, preprocessing, segmentation, feature extraction, classification, and post-processing.

Section 3: Data Acquisition
Description 3: This section describes the methods of data acquisition in online and offline recognition processes.

Section 4: Offline Documents
Description 4: This section details the preprocessing steps for offline documents, including binarization, noise removal, skeletonization, skew correction, and normalization.

Section 5: Online Documents
Description 5: This section outlines the preprocessing steps for online documents, such as dehooking, duplicate point elimination, smoothing, size normalization, and resampling.

Section 6: Segmentation
Description 6: This section elaborates on the techniques used to isolate individual characters from handwritten text for further processing.

Section 7: Feature Extraction
Description 7: This section covers the methods of feature extraction such as statistical, structural, and hybrid techniques used to form feature vectors for classification.

Section 8: Classification
Description 8: This section describes different classifiers used for recognizing characters based on extracted features including Bayesian classifier, Binary tree classifier, Nearest Neighbor classifier, Neural networks, MQDF, and Support Vector Machines.

Section 9: Post Processing
Description 9: This section discusses the post-processing steps including output representation in Unicode format, error correction, and disambiguation of confusing character pairs.

Section 10: Malayalam Script Features
Description 10: This section presents the unique features of the Malayalam script, its character set, and its historical background.

Section 11: Studies On Offline Malayalam Character Recognition
Description 11: This section reviews the notable works and techniques developed for offline recognition of Malayalam characters over the years.

Section 12: Studies On Online Malayalam Character Recognition
Description 12: This section reviews the significant studies and advancements in the online recognition of Malayalam characters.

Section 13: Conclusion & Future Works
Description 13: This section summarizes the findings, highlights the current best practices, discusses the challenges faced, and suggests directions for future research in Malayalam handwritten character recognition.