Handwritten Character Recognition of South Indian Scripts: A Review

Handwritten character recognition is always a frontier area of research in the field of pattern recognition and image processing and there is a large demand for OCR on hand written documents. Even though, sufficient studies have performed in foreign scripts like Chinese, Japanese and Arabic characters, only a very few work can be traced for handwritten character recognition of Indian scripts especially for the South Indian scripts. This paper provides an overview of offline handwritten character recognition in South Indian Scripts, namely Malayalam, Tamil, Kannada and Telungu.Comment: Paper presented on the "National Conference on Indian Language Computing", Kochi, February 19-20, 2011. 6 pages, 5 figure

Feature Extraction Techniques for Marathi Character Classification using Neural Networks Models

Hand written Marathi Character Recognition is challenges to the researchers due to the complex structure. This paper presents a novel approach for recognition of unconstrained handwritten Marathi characters. The recognition is carried out using multiple feature extraction methods and classification scheme. The initial stages of feature extraction are based upon the pixel value features and the classification of the characters is done according to the structural parameters into 44 classes. The final stage of feature extraction makes use of the zoning features. First Pixel values are used as features and these values are further modified as another set of features. All these features are then applied to neural network for recognition. A separate neural network is built for each type of feature. The average recognition rate is found to be 67.96% , 82.67%,63,46% and 76.46% respectively for feed forward , radial basis , elman and pattern recognition neural networks for handwritten marathi characters

Review on Optical Character Recognition of Devanagari Script Using Neural Network

During the last decades lot of research work has been done in the field of character recognition on various scripts in various languages. In India peoples are used to speak national language Hindi and spoken by more than 500 million people. Many languages in India, such as Hindi, Marathi and Sanskrit has uses Devanagari as its base script .As compared to English character; Indian script (Devanagri) characters are complicated for recognition. Devnagri script is the basis for many Indian script including Hindi, Sanskrit, Marathi, Kashmiri, and so on. In this paper we present a review of research work that has been done in the field of character recognition in Devanagari script in past

Benchmark Classification of Handwritten Dataset by New Operator

In recent years, many new classifiers and feature extraction algorithms were proposed and tested on various OCR databases and these techniques were used in wide applications. Various systematic papers and inventions in OCR were reported in the literature. We can say that OCR is one of the most important and active research areas in the pattern recognition. Today, research OCR is dealing with diverse a character of complex problems. Important research in OCR includes the text degraded (heavy noise) and analysis/recognition of complex documents (including texts, images, graphs, tables and video documents). In this proposed system we are suing a new operator Recognition of Devnagari handwritten Characters one of the biggest problem in present scenario. Devnagari characters are not recognized efficiently and truthfully by electronic device. Many researchers and algorithm have been proposed for recognizing of characters. For recognizing of characters, many processes have to be performed but no single technique or algorithm can perform that recognition and give more accurate result. objective of this dissertation work is to propose a new operator, the name of this operator is Kirsch Operator and algorithm for getting accurate result