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Proceedings Paper

Massively parallel implementation of character recognition systems
Author(s): Michael D. Garris; Charles L. Wilson; James L. Blue; Gerald T. Candela; Patrick J. Grother; Stanley A. Janet; R. Allen Wilkinson
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Paper Abstract

A massively parallel character recognition system has been implemented. The system is designed to study the feasibility of the recognition of handprinted text in a loosely constrained environment. The NIST handprint database, NIST Special Database 1, is used to provide test data for the recognition system. The system consists of eight functional components. The loading of the image into the system and storing the recognition results from the system are I/O components. In between are components responsible for image processing and recognition. The first image processing component is responsible for image correction for scale and rotation, data field isolation, and character data location within each field; the second performs character segmentation; and the third does character normalization. Three recognition components are responsible for feature extraction and character reconstruction, neural network-based character recognition, and low-confidence classification rejection. The image processing to load and isolate 34 fields on a scientific workstation takes 900 seconds. The same processing takes only 11 seconds using a massively parallel array processor. The image processing components, including the time to load the image data, use 94 of the system time. The segmentation time is 15 ms/character and segmentation accuracy is 89 for handprinted digits and alphas. Character recognition accuracy for medium quality machine print is 99.8. On handprinted digits, the recognition accuracy is 96 and recognition speeds of 10,100 characters/second can be realized. The limiting factor in the recognition portion of the system is feature extraction, which occurs at 806 characters/second. Through the use of a massively parallel machine and neural recognition algorithms, significant improvements in both accuracy and speed have been achieved, making this technology effective as a replacement for key data entry in existing data capture systems.

Paper Details

Date Published: 1 August 1992
PDF: 12 pages
Proc. SPIE 1661, Machine Vision Applications in Character Recognition and Industrial Inspection, (1 August 1992); doi: 10.1117/12.130294
Show Author Affiliations
Michael D. Garris, National Institute of Standards and Technology (United States)
Charles L. Wilson, National Institute of Standards and Technology (United States)
James L. Blue, National Institute of Standards and Technology (United States)
Gerald T. Candela, National Institute of Standards and Technology (United States)
Patrick J. Grother, National Institute of Standards and Technology (United States)
Stanley A. Janet, National Institute of Standards and Technology (United States)
R. Allen Wilkinson, National Institute of Standards and Technology (United States)


Published in SPIE Proceedings Vol. 1661:
Machine Vision Applications in Character Recognition and Industrial Inspection
Donald P. D'Amato; Wolf-Ekkehard Blanz; Byron E. Dom; Sargur N. Srihari, Editor(s)

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