Recognition of Words in Tamil Script Using Neural Network

Abstract

In this paper, word recognition using neural network is proposed. Recognition process is started with the partitioning of document image into lines, words, and characters and then capturing the local features of segmented characters. After classifying the characters, the word image is transferred into unique code based on character code. This code ideally describes any form of word including word with mixed styles and different sizes. Sequence of character codes of the word form input pattern and word code is a target value of the pattern. Neural network is used to train the patterns of the words. Trained network is tested with word patterns and is recognized or unrecognized based on the network error value. Experiments have been conducted with a local database to evaluate the performance of the word recognizing system and obtained good accuracy. This method can be applied for any language word recognition system as the training is based on only unique code of the characters and words belonging to the language.

Systematic Classification of Historical Handwritten Tamil Palm Leaf Manuscript using CART algorithm and RBF Network

“…So far most of the research for Ancient Tamil characters recognition has been done using image processing as in [1] [2]. Some Tamil printed document characters were classified using Neural Network as in [3] and some authors used images processing to segment the images as in [4]. Research work for Tamil palm-leaf manuscript digitization was very less and some work has been done using image processing as in [5] [6].…”