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We present two schemas for online recognition of Telugu characters, involving elaborate multiclassifier architectures. Considering the three-tier vertical organization of a typical Telugu character, we divide the stroke set into 4 subclasses primarily based on their vertical position. Stroke level recognition is based on a bank of Support Vector Machines (SVMs), with a separate SVM trained on each of these classes. Character recognition for Schema 1 is based on a Ternary Search Tree (TST), while for Schema 2 it is based on a SVM. The two schemas yielded overall stroke recognition performances of 89.59% and 96.69% respectively surpassing some of the recent online recognition performance results related to Telugu script reported in literature. The schemas yield character-level recognition performances of 90.55% and 96.42% respectively.

Added on September 5, 2017

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  • Author : Rajkumar.J ,Mariraja K.,Kanakapriya.K,Nishanthini. S ,Chakravarthy, V.S.

With the increasing popularity of pen-based digital devices, online handwriting recognition has generated potential markets in India. Hand-held devices equipped with pen-based technologies are now affordable to a large section of Indian population. However, till date little research has been done on online handwriting recognition of an Indian script. On the other hand, India is a multilingual country and Bangla is its second most popular language used by nearly 220 million people in India and Bangladesh. Unconstrained handwriting in Bangla is cursive in nature unlike other Indian scripts. Difficulties of designing a recognition system for unconstrained Bangla handwriting are mainly due to the fact that Bangla has a very large alphabet set consisting of nearly 300 shapes many of which are very complex and also there are a large number of shape similar characters. In this article, we describe preliminary results of our recent study on limited vocabulary Bangla cursive handwriting recognition based on somewhat unusual combination of multilayer perceptron (MLP) and support vector machine (SVM) classifiers. We simulated the proposed approach on two vocabularies of sizes 50 and 110 and the recognition performance on these two are comparable.

Added on September 5, 2017

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  • Author : Sk. Mohiuddin,Ujjwal Bhattacharya,Swapan K. Parui
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With the increasing popularity of pen-based digital devices, online handwriting recognition has generated potential markets in India. Hand-held devices equipped with pen-based technologies are now affordable to a large section of Indian population. However, till date little research has been done on online handwriting recognition of an Indian script. On the other hand, India is a multilingual country and Bangla is its second most popular language used by nearly 220 million people in India and Bangladesh. Unconstrained handwriting in Bangla is cursive in nature unlike other Indian scripts. Difficulties of designing a recognition system for unconstrained Bangla handwriting are mainly due to the fact that Bangla has a very large alphabet set consisting of nearly 300 shapes many of which are very complex and also there are a large number of shape similar characters. In this article, we describe preliminary results of our recent study on limited vocabulary Bangla cursive handwriting recognition based on somewhat unusual combination of multilayer perceptron (MLP) and support vector machine (SVM) classifiers. We simulated the proposed approach on two vocabularies of sizes 50 and 110 and the recognition performance on these two are comparable.

Added on September 5, 2017

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  • Author : Sk. Mohiuddin,Ujjwal Bhattacharya,Swapan K. Parui
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In this paper, we introduce a novel offline strategy for recognition of online handwritten Devanagari characters entered in an unconstrained manner. Unlike the previous approaches based on standard classifiers - SVM, HMM, ANN and trained on statistical, structural or spectral features, our method, based on CNN, allows writers to enter characters in any number or order of strokes and is also robust to certain amount of overwriting.

Added on September 5, 2017

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  • Author : Kapil Mehrotra,Saumya Jetley,Akash Deshmukh,Swapnil Belhe

In this paper, we have proposed an Adaptive Radial Basis Function Network (ARBFN) based writer adaptation for online handwriting recognition. We used RBF Network for both training (writer independent recognition system) and adaptation (writer dependent recognition system) using incremental learn¬ing. The adaptation is performed by either adding a center to the present network or modifying the weights of the existing proto¬type vectors using standard LMS gradient descent, whenever a misclassification is reported. ARBFN is evaluated on devanagari characters and numerals, collected in house.

Added on September 5, 2017

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  • Author : Surabhi Raje,Kapil Mehrotra ,Swapnil Belhe