Share Email Print
cover

Proceedings Paper

Symbol recognition without prior segmentation
Author(s): Badr Al-Badr; Robert M. Haralick
Format Member Price Non-Member Price
PDF $14.40 $18.00

Paper Abstract

We describe a new method for recognizing cursive and degraded text using OCR technology. Using this method, symbols on a page are identified by detecting primitives (parts of symbols), and then finding the best global grouping of primitives into symbols. On an image of text, primitives are detected using mathematical morphology operations, in a way that does not require or involve a prior segmentation step. This paper lays out the overall strategy of a system that implements the recognition method. A following paper reports on experimental protocols and results. This system has three major features: (1) by globally optimizing the process of combining primitives into symbols, it is robust and less sensitive to noise; (2) it does not require segmenting a text block into lines, a line into words, nor a word into characters; and (3) it is language independent in that training determines the symbol set it recognizes.

Paper Details

Date Published: 23 March 1994
PDF: 12 pages
Proc. SPIE 2181, Document Recognition, (23 March 1994); doi: 10.1117/12.171118
Show Author Affiliations
Badr Al-Badr, Univ. of Washington (United States)
Robert M. Haralick, Univ. of Washington (United States)


Published in SPIE Proceedings Vol. 2181:
Document Recognition
Luc M. Vincent; Theo Pavlidis, Editor(s)

© SPIE. Terms of Use
Back to Top