A hybrid feature extraction framework for handwritten numeric fields recognition

Jung Hsien Chiang, Paul Gader

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

A hybrid feature extraction framework for handwritten numeric fields recognition is described. The numeric fields were extracted from binary images of credit card application forms. The images include identity numbers (ID) and phone numbers. The feature extraction framework utilizes a cascade of a Kohonen self-organizing feature map (SOM) and a set of non-linear filtering units. The goals of our feature extraction process are to provide reliable information to the recognition stage. The recognition stage uses the feature set as inputs to a multi-layer neural network. We present experimental results which demonstrate the ability to extract features automatically in handwritten digit recognition. Experiments were performed on a test data set from the CCL/ITRI Database which consists of over 90,390 handwritten numeric digits. Recognition rate of 98.6% is achieved on this database.

Original languageEnglish
Title of host publicationTrack D
Subtitle of host publicationParallel and Connectionist Systems
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages436-440
Number of pages5
ISBN (Print)081867282X, 9780818672828
DOIs
Publication statusPublished - 1996 Jan 1
Event13th International Conference on Pattern Recognition, ICPR 1996 - Vienna, Austria
Duration: 1996 Aug 251996 Aug 29

Publication series

NameProceedings - International Conference on Pattern Recognition
Volume4
ISSN (Print)1051-4651

Other

Other13th International Conference on Pattern Recognition, ICPR 1996
CountryAustria
CityVienna
Period96-08-2596-08-29

All Science Journal Classification (ASJC) codes

  • Computer Vision and Pattern Recognition

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