An experimental HMM-based postal OCR system

It is almost universally accepted in speech recognition that phone- or word-level segmentation prior to recognition is neither feasible nor desirable, and in the dynamic (pen-based) handwriting recognition domain the success of segmentation-free techniques points to the same conclusion. But in image-based handwriting recognition, this conclusion is far from being firmly established, and the results presented in this paper show that systems employing character-level presegmentation can be more effective, even within the same HMM paradigm, than systems relying on sliding window feature extraction. We describe two variants of a Hidden Markov system recognizing handwritten addresses on US mail, one with presegmentation and one without, and report results on the CEDAR data set. 1. INTRODUCTION Any approach to speech and handwriting recognition must take into account that the signal is composed from a succession of alphabetic units (phonemes or graphemes). In the early work on speech recog..

A high performance domain specific OCR for Bangla script

Includes bibliographical references (page 5).Abstract-Research on recognizing Bengali script has been started since mid 1980’s. A variety of different techniques have been applied and the performance is examined. In this paper we present a high performance domain specific OCR for recognizing Bengali script. We select the training data set from the script of the specified domain. We choose Hidden Markov Model (HMM) for character classification due to its simple and straightforward way of representation. We examine the primary error types that mainly occurred at preprocessing level and carefully handled those errors by adding special error correcting module as a part of recognizer. Finally we added a dictionary and some error specific rules to correct the probable errors after the word formation is done. The entire technique significantly increases the performance of the OCR for a specific domain to a great extent

A Comparative study of Arabic handwritten characters invariant feature

This paper is practically interested in the unchangeable feature of Arabic handwritten character. It presents results of comparative study achieved on certain features extraction techniques of handwritten character, based on Hough transform, Fourier transform, Wavelet transform and Gabor Filter. Obtained results show that Hough Transform and Gabor filter are insensible to the rotation and translation, Fourier Transform is sensible to the rotation but insensible to the translation, in contrast to Hough Transform and Gabor filter, Wavelets Transform is sensitive to the rotation as well as to the translation