IMPROVEMENT IN HANDWRITTEN NUMERAL STRING RECOGNITION BY SLANT NORMALIZATION AND CONTEXTUAL INFORMATION

Alceu de S. BRITTO JR.

Pontifícia Universidade Católica do Paraná (PUC­PR), R. Imaculada Conceição, 1155 Curitiba (PR) 80215­901 -- Brazil Universidade Estadual de Ponta Grossa (UEPG), Praça Santos Andrade S/N, Centro, Ponta Grossa (PR) 84100­000 -- Brazil
E­mail: alceu@cenparmi.concordia.ca

Robert SABOURIN

École de Technologie Supérieure (ETS), 1100 Rue Notre Dame Ouest Montreal (QC) H3C 1K3 ­ Canada Centre for Pattern Recognition and Machine Intelligence (CENPARMI), 1455 de Maisonneuve Blvd. West, Suite GM 606 ­ Montreal (QC) H3G 1M8 ­ Canada
E­mail: sabourin@gpa.etsmtl.ca

Edouard LETHELIER and Flavio BORTOLOZZI

Pontifícia Universidade Católica do Paraná (PUC­PR), R. Imaculada Conceição, 1155 Curitiba (PR) 80215­901 ­ Brazil
E­mail: {edouard, fborto}@ppgia.pucpr.br

Ching Y. SUEN

Centre for Pattern Recognition and Machine Intelligence (CENPARMI), 1455 de Maisonneuve Blvd. West, Suite GM 606 ­ Montreal (QC) H3G 1M8 ­ Canada
E­mail: suen@cenparmi.concordia.ca

This work describes a way of enhancing handwritten numeral string recognition by considering slant normalization and contextual information to train an implicit segmentation­based system. A word slant normalization method is modified in order to improve the results for handwritten numeral strings. We assume that each connected component (CC) in the string has its own slant. The slant and contour length of each CC are used for obtaining the mean slant of the string. Both the original and modified methods are evaluated by means of some interesting analyses on the NIST SD19 database. These analyses show (a) the positive impact of slant correction on the number of overlapping numerals in strings, and (b) the difference in normalizing isolated numerals based on the slant estimated from their own images and the slant estimated from their original string images. Slant normalization and contextual information regarding string slant and digit size variations within the string are used to train numeral HMMs. Preliminary string recognition results, produced by a system under construction, are shown.

In: L.R.B. Schomaker and L.G. Vuurpijl (Eds.)
Proceedings of the Seventh International Workshop on Frontiers
in Handwriting Recognition, September 11-13 2000, Amsterdam,
Nijmegen: International Unipen Foundation,
ISBN 90-76942-01-3
pp. 323-332.