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 classied. 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.

https://hal.inria.fr/inria-00104402/document

Online Handwritten Character Recognition of Devanagari and Telugu Characters using Support Vector Machines

In this paper we propose an approach to recognize handwritten Tamil characters using a multilayer perceptron with one hidden layer. The feature extracted from the handwritten character is Fourier descriptors. Also an analysis was carried out to determine the number of hidden layer nodes to achieve high performance of backpropagation network in the recognition of handwritten Tamil characters. The system was trained using several different forms of handwriting provided by both male and female participants of different age groups. Test results indicate that Fourier descriptors combined with backpropagation network provide good recognition accuracy of 97% for handwritten Tamil characters.

Neural Network Based Offline Tamil Handwritten Character Recognition System

Hidden Markov Models (HMM) have long been a popu- lar choice for Western cursive handwriting recognition fol- lowing their success in speech recognition. Even for the recognition of Oriental scripts such as Chinese, Japanese and Korean, Hidden Markov Models are increasingly being used to model substrokes of characters. However, when it comes to Indic script recognition, the published work em- ploying HMMs is limited, and generally focussed on iso- lated character recognition. In this effort, a data-driven HMM-based online handwritten word recognition system for Tamil, an Indic script, is proposed. The accuracies obtained ranged from 98% to 92.2% with different lexicon sizes (1K to 20K words). These initial results are promising and warrant further research in this direction. The results are also encouraging to explore possibilities for adopting the approach to other Indic scripts as well.

http://www.hpl.hp.com/techreports/2007/HPL-2007-108.pdf

Hidden Markov Models for Online Handwritten Tamil Word Recognition

A collection of different scripts is employed in writing languages throughout the world. Character and numeral recognition of a particular script is a key area in the field of pattern recognition. In this paper, we have presented a comprehensive survey on character and numeral recognition of non-Indic and Indic scripts. Many researchers have done work on character and numeral recognition from the most recent couple of years. In perspective of this, few strategies for character/numeral have been developed so far. There are an immense number of frameworks available for printed and handwritten character recognition for non-Indic scripts. But, only a limited number of systems are offered for character/numeral recognition of Indic scripts. However, few endeavors have been made on the recognition of Bangla, Devanagari, Gurmukhi, Kannada, Oriya and Tamil scripts. In this paper, we have additionally examined major challenges/issues for character/numeral recognition. The efforts in two directions (non-Indic and Indic scripts) are reflected in this paper. When compared with non-Indic scripts, the research on character recognition of Indic scripts has not achieved that perfection yet. The techniques used for recognition of non-Indic scripts may be used for recognition of Indic scripts (printed/handwritten text) and vice versa to improve the recognition rates. It is also noticed that the research in this field is quietly thin and still more research is to be done, particularly in the case of handwritten Indic scripts documents.

Character and numeral recognition for non-Indic and Indic scripts: a survey

For automatic recognition of Bangla script, only a few studies are reported in the literature, which is in contrast to the role of Bangla as one of the world's major scripts. In this paper we present a new approach to online Bangla handwriting recognition and one of the first to consider cursively written words instead of isolated characters. Our method uses a sub-stroke level feature representation of the script and a writing model based on hidden Markov models. As for the latter an appropriate internal structure is crucial, we investigate different approaches to defining model structures for a highly compositional script like Bangla. In experimental evaluations of a writer independent Bangla word recognition task we show that the use of context-dependent sub-word units achieves quite promising results and significantly outperforms alternatively structured models.

Online Bangla Word Recognition Using Sub-Stroke Level Features and Hidden Markov Models

A system for online recognition of handwritten Tamil characters is presented. A handwritten character is constructed by executing a sequence of strokes. A structure- or shape-based representation of a stroke is used in which a stroke is represented as a string of shape features. Using this string representation, an unknown stroke is identified by comparing it with a database of strokes using a flexible string matching procedure. A full character is recognized by identifying all the component strokes. Character termination, is determined using a finite state automaton. Development of similar systems for other Indian scripts is outlined.

/pdf/online-handwriting-recognition-for-tamil-f7p2tbzq23.pdf

K.H. Aparna

Papers

Online handwriting recognition for Tamil