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In this paper, we present our recent study of a data driven approach to combining multiple SVM classifiers with RBF kernels each being trained with a distinct feature vector. The SVM classifiers in our ensemble are ranked based on their increasing order of average performance on the validation sample sets. The outputs of the SVM classifiers are combined based on a weighted average strategy which uses the above ranks of the underlying SVMs to determine the respective weights. In the present study, we design four sets of different feature vectors representing online handwritten words. Simple enation of these feature vectors does not help much in improving the recognition accuracy compared to the best performing feature vector among the four.

Added on September 5, 2017

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  • Contributed by : OHWR Consortium
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  • Author : A. Srimany ,S. Dutta Chowdhuri,U. Bhattacharya,S. K. Parui
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A reasonably large database of online handwritten Bangla characters has been developed. Such a handwritten character sample is composed of one or more strokes. Seventy five such stroke classes have been identified on the basis of the varying handwriting styles present in the character database. Each character sample is a sequence of strokes emanating from these stroke classes. Another database of handwritten Bangla strokes has been developed from the character database. This is the first such database for Bangla script. Certain stroke level features are defined on the basis of certain extremum points which represent the stroke shape reasonably well.

Added on September 5, 2017

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  • Contributed by : OHWR consortium
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  • Author : Chandan Biswas,Ujjwal Bhattacharya,Swapan Kumar Parui
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This work describes the development of Assamese online numeral recognition system using Hidden Markov Models (HMM) and Support Vector Machines (SVM). Preprocessed (x,y) coordinates and their first and second derivatives at each point are used as features for both the modeling techniques. The two systems are developed individually using HMM and SVM. The results from both the systems are then combined using two different approaches. In the first approach, the scores from both the classifiers are directly merged and an improvement in performance is observed in the combined system (comb−1). In the second approach,the confusion patterns from HMM and SVM classifiers are also analyzed. Based on this, the results are further combined to obtain a final hybrid numeral recognizer with anenhanced performance (comb−2). The HMM, SVM, Comb-1and Comb-2 systems provide average recognition performance of 96.5, 96.8, 98 and 98.3, respectively.

Added on September 5, 2017

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  • Author : Bandita Sarma,R. Krishna Naik,S.R.M.Prasanna,Chitralekha Mahanta,Kapil Mehrotra,Swapnil Belhe

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