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Proceedings Paper

Gradient-based contour encoding for gray-scale character recognition
Author(s): Geetha Srikantan; Stephen W. Lam; Sargur N. Srihari
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Paper Abstract

We present a fast new algorithm for grayscale character recognition. By operating on grayscale images directly, we attempt to maximize the information that can be extracted. Traditional recognition based on binarization of images is also avoided. Previous work using gradient-based contour encoding was used in a restricted domain where objects were size- invariant and fewer patterns had to be distinguished; a simpler feature set sufficed, in this domain. In character recognition, a feature-extractor has to handle character images of arbitrary size. Our algorithm extracts local contour features based on the pixel gradients present in an image. A gradient map, i.e., gradient magnitude and direction at every pixel, is computed; this map is thresholded to avoid responses to noise and spurious artifacts, the map is then partitioned coarsely. Character contours are encoded by quantizing gradient directions into a few representative direction bins. This method does not require that images be normalized to a fixed grid-size before feature extraction. Features extracted by this method have been used to train a 2-layer neural network classifier. Experiments with machine-printed numeric (10-class), and alphanumeric & punctuation (77-class) character images, captured at 300 ppi from mailpiece images, have been conducted. Currently, 99.4% numerals and 93.4% from 77-class images were recognized correctly from a testing set of 5420 numeric and 24,988 character images. When shape and case confusions are allowed, the recognition performance improves to 97.5%. A similar experiment with binarized 77-class images resulted in 92.1% correctly recognized test images. This method is being extended to handwritten characters recognition.

Paper Details

Date Published: 14 April 1993
PDF: 8 pages
Proc. SPIE 1906, Character Recognition Technologies, (14 April 1993); doi: 10.1117/12.143609
Show Author Affiliations
Geetha Srikantan, SUNY/Buffalo (United States)
Stephen W. Lam, SUNY/Buffalo (United States)
Sargur N. Srihari, SUNY/Buffalo (United States)

Published in SPIE Proceedings Vol. 1906:
Character Recognition Technologies
Donald P. D'Amato, Editor(s)

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