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Proceedings Paper

A Fourier-descriptor-based character recognition engine implemented under the Gamera open-source document-processing framework
Author(s): Jared Hopkins; Tim L. Andersen
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Paper Abstract

This paper discusses the implementation of an engine for performing optical character recognition of bi-tonal images using the Gamera framework, an existing open-source framework for building document analysis applications. The OCR engine uses features that are based on the Fourier descriptor to distinguish characters, and is designed to be able to handle character images that contain multiple boundaries. The algorithm works by assigning to each character image a signature that encodes the boundary types that are present in the image as well as the positional relationships that exist between them. Under this approach, only images having the same signature are comparable. Effectively, a meta-classifier is used which first computes the signature of an input image and then dispatches the image to an underlying neural network based classifier which is trained to distinguish between images having that signature. The performance of the OCR engine is evaluated on a set of sample images taken from the newspaper domain, and compares well with other OCR engines. The source code for this engine and all supporting modules is currently available upon request, and will eventually be made available through an open-source project on the sourceforge website.

Paper Details

Date Published: 17 January 2005
PDF: 8 pages
Proc. SPIE 5676, Document Recognition and Retrieval XII, (17 January 2005); doi: 10.1117/12.589218
Show Author Affiliations
Jared Hopkins, Boise State Univ. (United States)
Tim L. Andersen, Boise State Univ. (United States)


Published in SPIE Proceedings Vol. 5676:
Document Recognition and Retrieval XII
Elisa H. Barney Smith; Kazem Taghva, Editor(s)

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