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Proceedings Paper

Unconstrained handprint recognition using a limited lexicon
Author(s): Michael D. Garris
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Paper Abstract

A word recognition system has been developed at NIST to read free-formatted text paragraphs containing handprinted characters. The system has been developed and tested using binary images containing 2,100 different writers' printings of the Preamble to the U.S. Constitution. Each writer was asked to print these sentences in an empty 70 mm by 175 mm box. The Constitution box contains no guidelines for the placement and spacing of the handprinted text, nor are there guidelines to instruct the writer where to stop printing one line and to begin the next. While the layout of the handprint in these paragraphs is unconstrained, a limited-size lexicon may be applied to reduce the complexity of the recognition application. The system's four components have been combined into an end-to-end hybrid system that executes across a UNIX file server and a massively parallel SIMD computer. The recognition system achieves a word error rate of 49% across all 2,100 printings of the Preamble (109,096 words). This performance is achieved with a neural network character classifier that has a substitution error rate of 14% on its 22,823 training patterns.

Paper Details

Date Published: 23 March 1994
PDF: 11 pages
Proc. SPIE 2181, Document Recognition, (23 March 1994); doi: 10.1117/12.171129
Show Author Affiliations
Michael D. Garris, National Institute of Standards and Technology (United States)

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

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