TY - GEN
T1 - Performance improvement of dot-matrix character recognition by variation model based learning
AU - Endo, Koji
AU - Ohyama, Wataru
AU - Wakabayashi, Tetsushi
AU - Kimura, Fumitaka
PY - 2015/1/1
Y1 - 2015/1/1
N2 - This paper describes an effective learning technique for optical dot-matrix characters recognition. Automatic reading system for dotmatrix character is promising for reduction of cost and labor required for quality control of products. Although dot-matrix characters are constructed by specific dot patterns, variation of character appearance due to three-dimensional rotation of printing surface, bleeding of ink and missing parts of character is not negligible. The appearance variation causes degradation of recognition accuracy. The authors propose a technique improving accuracy and robustness of dot-matrix character recognition against such variation, using variation model based learning. The variation model based learning generates training samples containing four types of appearance variation and trains a Modified Quadratic Discriminant Function (MQDF) classifier using generated samples. The effectiveness of the proposed learning technique is empirically evaluated with a dataset which contains 38 classes (2030 character samples) captured from actual products by standard digital cameras. The recognition accuracy has been improved from 78.37% to 98.52% by introducing the variation model based learning.
AB - This paper describes an effective learning technique for optical dot-matrix characters recognition. Automatic reading system for dotmatrix character is promising for reduction of cost and labor required for quality control of products. Although dot-matrix characters are constructed by specific dot patterns, variation of character appearance due to three-dimensional rotation of printing surface, bleeding of ink and missing parts of character is not negligible. The appearance variation causes degradation of recognition accuracy. The authors propose a technique improving accuracy and robustness of dot-matrix character recognition against such variation, using variation model based learning. The variation model based learning generates training samples containing four types of appearance variation and trains a Modified Quadratic Discriminant Function (MQDF) classifier using generated samples. The effectiveness of the proposed learning technique is empirically evaluated with a dataset which contains 38 classes (2030 character samples) captured from actual products by standard digital cameras. The recognition accuracy has been improved from 78.37% to 98.52% by introducing the variation model based learning.
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U2 - 10.1007/978-3-319-16631-5_11
DO - 10.1007/978-3-319-16631-5_11
M3 - Conference contribution
AN - SCOPUS:84942540385
SN - 9783319166308
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 147
EP - 156
BT - Computer Vision - ACCV 2014 Workshops, Revised Selected Papers
A2 - Jawahar, C.V.
A2 - Shan, Shiguang
PB - Springer Verlag
T2 - 12th Asian Conference on Computer Vision, ACCV 2014
Y2 - 1 November 2014 through 5 November 2014
ER -