Symbol recognition without prior segmentation

Badr Al-Badr; Robert M. Haralick

doi:10.1117/12.171118

23 March 1994 Symbol recognition without prior segmentation

Badr Al-Badr, Robert M. Haralick

Proceedings Volume 2181, Document Recognition; (1994) https://doi.org/10.1117/12.171118
Event: IS&T/SPIE 1994 International Symposium on Electronic Imaging: Science and Technology, 1994, San Jose, CA, United States

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.

Citation Download Citation

Badr Al-Badr and Robert M. Haralick "Symbol recognition without prior segmentation", Proc. SPIE 2181, Document Recognition, (23 March 1994); https://doi.org/10.1117/12.171118

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