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In this article, we propose a novel scheme for online handwritten character recognition based on Levenshtein distance metric. Both shape and position information are considered in our feature representation scheme. The shape information is encoded by a string of quantized values of angular displacements between successive sample points along the trajectory of the handwritten character. The consecutive occurrences of same value in such a string are removed retaining only one of them. Next, each element in the resulting string is assigned an integral weight value proportional to the length of the segment of the trajectory represented by the corresponding element.

Added on September 6, 2017

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  • Author : S. Dutta Chowdhury,U. Bhattacharya,S. K. Parui
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A system for recognition of online handwritten characters has been presented for Indian writing systems. A handwritten character is represented as a sequence of strokes whose features are extracted and classified. Support vector machines have been used for constructing the stroke recognition engine. The results have been presented after testing the system on Devanagari and Telugu scripts.

Added on September 6, 2017

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  • Author : H.Swethalakshmi,Anitha Jayaraman & C. Chandra Sekhar,V. Srinivasa Chakravarthy

In this article, we aim at reducing the error rate of the Tamil symbol recognition system by employing multiple experts to reevaluate certain decisions of the primary Support Vector Machine (SVM) classifier. Motivated by the relatively high percentage of occurrence of base consonants in the script, a reevaluation technique has been proposed to correct any ambiguities arising in the base consonants. Secondly, a dynamic time warping method is proposed to automatically extract the discriminative regions for each set of confused characters. Class-specific features derived from these regions aid in reducing the degree of confusions.

Added on September 6, 2017

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  • Author : Suresh Sundaram,A G Ramakrishnan
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This paper addresses the problem of resolving ambiguities in frequently confused online Tamil character pairs by employing script specific algorithms as a post classification step. Robust structural cues and temporal information of the preprocessed character are extensively utilized in the design of these algorithms. The methods are quite robust in automatically extracting the discriminative substrokes of confused characters for further analysis. Experimental validation on the IWFHR Database indicates error rates of less than 3 % for the confused characters. Thus, these post processing steps have a good potential to improve the performance of online Tamil handwritten character recognition.

Added on September 6, 2017

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  • Author : A G Ramakrishnan,Suresh Sundaram

The most general form of handwriting style is mixed cursive and this is the most difficult type in view of its automatic recognition. Similar handwriting style are prevalent in various scripts such as English, Arabic, Bengali etc. Handwriting recognition for such a script gets further difficult whenever its alphabet consists of a large number of characters like Bengali which has around 350 characters. Hidden Markov models (HMM) are the most popularly used architectures for similar recognition problems. However, the task becomes easy if the underlying lexicon depending upon the specific application is provided.

Added on September 6, 2017

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  • Author : Oendrila Samanta ,Anandarup Roy,Ujjwal Bhattacharya,Swapan K. Parui
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