Abstract

This paper presents a script-independent methodology for optical character recognition (OCR) based on the use of hidden Markov models (HMM). The feature extraction, training and recognition components of the system are all designed to be script independent. The training and recognition components were taken without modification from a continuous speech recognition system; the only component that is specific to OCR is the feature extraction component. To port the system to a new language, all that is needed is text image training data from the new language, along with ground truth which gives the identity of the sequences of characters along each line of each text image, without specifying the location of the characters on the image. The parameters of the character HMMs are estimated automatically from the training data, without the need for laborious handwritten rules. The system does not require …

Date

2001

Authors

Premkumar Natarajan, Zhidong Lu, Richard Schwartz, Issam Bazzi, John Makhoul

Journal

International Journal of Pattern Recognition and Artificial Intelligence

Volume

15

Issue

01

Pages

43-63

Publisher

World Scientific Publishing Company