Design of a handprint recognition system

Michael D. Garris; James L. Blue; Gerald T. Candela; Darrin L. Dimmick; Jon C. Geist; Patrick J. Grother; Stanley A. Janet; Omid M. Omidvar; Charles L. Wilson

doi:10.1117/12.266865

1 April 1997 Design of a handprint recognition system

Michael D. Garris, James L. Blue, Gerald T. Candela, Darrin L. Dimmick, Jon C. Geist, Patrick J. Grother, Stanley A. Janet, Omid M. Omidvar, Charles L. Wilson

Author Affiliations +

Journal of Electronic Imaging, Vol. 6, Issue 2, (April 1997). https://doi.org/10.1117/12.266865

Abstract

A public domain optical character recognition (OCR) system has been developed by the National Institute of Standards and Technology (NIST). This standard reference form-based handprint recognition system is designed to provide a baseline of performance on an open application. The system’s source code, training data, performance assessment tools, and type of forms processed are all publicly available. The system is modular, allowing for system component testing and comparisons, and it can be used to validate training and testing sets in an end-to-end application. The system's source code is written in C and will run on virtually any UNIX-based computer. The presented functional components of the system are divided into three levels of processing: (1) form-level processing includes the tasks of form registration and form removal; (2) field-level processing includes the tasks of field isolation, line trajectory reconstruction, and field segmentation; and (3) character-level processing includes character normalization, feature extraction, character classification, and dictionary-based postprocessing. The system contains a number of significant contributions to OCR technology, including an optimized probabilistic neural network (PNN) classifier that operates a factor of 20 times faster than traditional software implementations of the algorithm. Provided in the system are a host of data structures and low-level utilities for computing spatial histograms, least-squares fitting, spatial zooming, connected components, Karhunen Loève feature extraction, optimized PNN classification, and dynamic string alignment. Any portion of this standard reference OCR system can be used in commercial products without restrictions.

Citation Download Citation

Michael D. Garris, James L. Blue, Gerald T. Candela, Darrin L. Dimmick, Jon C. Geist, Patrick J. Grother, Stanley A. Janet, Omid M. Omidvar, and Charles L. Wilson "Design of a handprint recognition system," Journal of Electronic Imaging 6(2), (1 April 1997). https://doi.org/10.1117/12.266865

Published: 1 April 1997

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available