Paper
23 March 1994 Disambiguation and spelling correction for a neural network based character recognition system
John M. Trenkle, Robert C. Vogt III
Author Affiliations +
Proceedings Volume 2181, Document Recognition; (1994) https://doi.org/10.1117/12.171120
Event: IS&T/SPIE 1994 International Symposium on Electronic Imaging: Science and Technology, 1994, San Jose, CA, United States
Abstract
Various approaches have been proposed over the years for using contextual and linguistic information to improve the recognition rates of existing OCR systems. However, there is an intermediate level of information that is currently underutilized for this task: confidence measures derived from the recognition system. This paper describes a high-performance recognition system that utilizes identification of field type coupled with field-level disambiguation and a spell-correction algorithm to significantly improve raw recognition outputs. This paper details the implementation of a high-accuracy machine-print character recognition system based on backpropagation neural networks. The system makes use of neural net confidences at every stage to make decisions and improve overall performance. It employs disambiguation rules and a robust spell-correction algorithm to enhance recognition. These processing techniques have led to substantial improvements of recognition rates in large scale tests on images of postal addresses.
© (1994) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
John M. Trenkle and Robert C. Vogt III "Disambiguation and spelling correction for a neural network based character recognition system", Proc. SPIE 2181, Document Recognition, (23 March 1994); https://doi.org/10.1117/12.171120
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Cited by 6 scholarly publications and 1 patent.
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KEYWORDS
Optical character recognition

Neural networks

Detection and tracking algorithms

Image segmentation

Feature extraction

Binary data

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