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A frequency count based two stage classification approach is proposed by combining generative and discriminative modeling principles for online handwritten character recognition.The first stage classifier based on Hidden Markov Model (HMM) returns top-K ranking characters out of the total N classes. In the second stage, pairwise classifiers for K (K−1) /2 unique combinations of top-K characters using Support Vector Machine (SVM) are developed. Usually pairwise classifiers are trained for most confused character pairs, by analyzing the confusion matrix.

Added on September 5, 2017

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  • Contributed by : OHWR Consortium
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  • Author : Subhasis Mandal,Himakshi Choudhury ,S. R. Mahadeva Prasanna,Suresh Sundaram

Hidden Markov Models (HMM) are used in handwritten strokes recognition task. The two design parameters of HMM are the number of states and number of mixtures in each state. There are two approaches for finding the number of states, namely, equal number of states and variable number of states. Since the shape of strokes will be different, variable number of states approach should be beneficial. This work proposes a curvature point detection based method to predict variable number of states for modeling a handwritten stroke. The proposed method selects appropriate points from a trace so that the portion between two consecutive points is modeled as an HMM state. Accordingly, based upon handwritten stroke shape complexity, the number of appropriate points selected will change and hence the number of states assigned to the corresponding stroke. In the proposed method, the number of states is proportional to the shape complexity of the given stroke as opposed to fixed in case of brute-force. The HMM based stroke recognizer consisting of 181 distinct strokes, was trained on a set of 52,977 examples collected from approximately 100 native Assamese writers. The evaluation was done on 43,828 examples collected from same users in different sessions. The experimental results demonstrate the benefits of the proposed technique over the brute-force method, especially in case of complex shape strokes.

Added on September 4, 2017

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  • Author : Subhasis Mandal,S. R. Mahadeva Prasanna,Suresh Sundaram

Hidden Markov Models (HMM) are the widely used modeling techniques for online handwriting recognition. This paper describes both stroke based and character based methods for Assamese handwritten character recognition using HMM classifier. In stroke based method, unique strokes that are used to write the characters are grouped and then HMM modeling is done for each of these selected class of strokes. A character can comprise of one or multiple strokes. Reference set is prepared by analyzing the different combinations of strokes and the degree of confusion between similar strokes. The stroke based method comprises of two stages. First, the stroke sequences in the test character is recognized by stroke based HMM classifier and in the second stage this sequence of strokes is compared against the entries of the reference set. The character corresponding to the matched stroke sequence in the reference set is considered as the recognized character. In character based method, each character as a whole is modeled using HMM and the classifier directly predicts the character class. Experiments were performed on 141 Assamese characters, collected from 100 native Assamese writers and it is observed that character based method gives better result than stroke based method.

Added on September 4, 2017

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  • Author : Himakshi Choudhury ,Subhasis Mandal, Sanjeevan Devnath,S. R. M. Prasanna,S. Sundaram

Hidden Markov Models (HMMs) and Support Vector Machine (SVM) based classifiers are commonly used in the field of handwriting recognition. In this paper we investigate a technique of recognizing Assamese handwritten characters using HMMs and SVM stroke classifiers in conjunction to each other. The two classifiers are separately trained on same stroke dataset with same set of features. The top-N class labels from the HMM classifier are selected and the search space of the SVM classifier is reduced by inspecting the SVM scores for the selected N classes only. The class with highest SVM score among these N classes is the predicted class. However the confidence score from the classifier is low for a predicted class if there exist some confusion with similar classes. In such cases a fall back option to consider the decision of the HMM classifier is introduced depending on the confidence score from the SVM classifier. In this way we select decision from one of the classifier to increase the stroke recognition rate. We have observed that better recognition performance can be achieved by proposed method. Finally for recognition of a test character, the recognized stroke set from the combined classifier is matched with the stroke sequences in a lookup table or reference set. The experiments are performed in a large number of handwritten Assamese characters collected from 100 native writers.

Added on September 4, 2017

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  • Author : Himakshi Choudhury,Subhasis Mandal,Sanjeevan Devnath,S. R. Mahadeva Prasanna,Suresh Sundaram

This work describes the development of an Assamese handwritten numeral recognizer. Online handwritten numeral recognition system is developed using x,y coordinates as the feature and Hidden Markov Model (HMM) as the modeling technique. Offline handwritten numeral recognition system is developed using vertical projection profile and horizontal projection profile (VPP-HPP), zonal discrete cosine transform (DCT), chain code histogram (CCH) and pixel level information as features and vector quantization (VQ) as the modelling technique. The confusion patterns of online and offline systems are analysed. Based on this, the two systems are further combined to obtain a final numeral recognition system. The combined system exhibits improved performance over the individual approaches, demonstrating the significance of different natures of information present in each mode.

Added on September 4, 2017

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  • Contributed by : OHWR Consortium
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  • Author : G. Siva Reddy, Puspanjali Sharma,S. R. M. Prasanna,C. Mahanta ,L N Sharma