Abstract

We describe BBN’s script-independent methodology for multilingual machine-print OCR and offline handwriting recognition (HWR) 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 HMM training and recognition components are based on BBN’s Byblos hidden Markov modeling software. The HMM parameters are estimated automatically from the training data, without the need for laborious manually created rules. The system does not require any pre-segmentation of the data, either at the word level or at the character level. Thus, the system can handle languages with cursive handwritten scripts in a straightforward manner. The script independence of the system is demonstrated with experimental results in three scripts that exhibit significant differences in glyph characteristics: Arabic …

Date

2012

Authors

Premkumar Natarajan, Rohit Prasad, Huaigu Cao, Krishna Subramanian, Shirin Saleem, David Belanger, Shiv Vitaladevuni, Matin Kamali, Ehry MacRostie

Journal

Guide to OCR for Arabic Scripts

Pages

485-505

Publisher

Springer London