IMPLEMENTATION OF TAMIL-OCR USING NEURAL NETWORK
Abstract
The aim of the project is to develop OCR software for Tamil character recognition. OCR is an optical character recognition and is the mechanical or electronic translation of images of typewritten or handwritten (usually captured by a scanner) into machine-editable text. OCR is a field of research in pattern recognition, artificial intelligence and machine vision. Character recognition is used most often to describe the ability of computer to translate printer or human writing into text. In this paper we focus on recognition of English alphabet in a given scanned text document with the help of Neural Networks. Using Mat labNeural Network toolbox, we are going to recognize handwritten characters by projecting them on different sized grids. The first step is image acquisition which acquires the scanned image followed by noise filtering, smoothing and normalization of scanned image, rendering image suitable for segmentation where image is decomposed into sub images. Feature Extraction improves recognition rate and misclassification. We going to use character extraction and edge detection algorithm for training the neural network to classify and recognize the handwritten character. Existing Applications which are similar to our application contain many mismatches and errors that will be rectified in our project which increases the accuracy of the text character recognition.
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Project paper for research and paper presentation.
IEEE Paper for final year project
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IMPLEMENTATION OF TAMIL-OCR USING NEURAL NETWORK fULL PAPE R DOWNLOAD
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