Partitioning of the degradation space for OCR training

Elisa H. Barney Smith; Tim Andersen

doi:10.1117/12.641229

16 January 2006 Partitioning of the degradation space for OCR training

Elisa H. Barney Smith, Tim Andersen

Proceedings Volume 6067, Document Recognition and Retrieval XIII; 606705 (2006) https://doi.org/10.1117/12.641229
Event: Electronic Imaging 2006, 2006, San Jose, California, United States

Abstract

Generally speaking optical character recognition algorithms tend to perform better when presented with homogeneous data. This paper studies a method that is designed to increase the homogeneity of training data, based on an understanding of the types of degradations that occur during the printing and scanning process, and how these degradations affect the homogeneity of the data. While it has been shown that dividing the degradation space by edge spread improves recognition accuracy over dividing the degradation space by threshold or point spread function width alone, the challenge is in deciding how many partitions and at what value of edge spread the divisions should be made. Clustering of different types of character features, fonts, sizes, resolutions and noise levels shows that edge spread is indeed shown to be a strong indicator of the homogeneity of character data clusters.

Citation Download Citation

Elisa H. Barney Smith and Tim Andersen "Partitioning of the degradation space for OCR training", Proc. SPIE 6067, Document Recognition and Retrieval XIII, 606705 (16 January 2006); https://doi.org/10.1117/12.641229

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