Summary
|
For Full-Text PDF, please login, if you are a member of IEICE, or go to Pay Per View on menu list, if you are a nonmember of IEICE. |
|
Handwritten Numeral String Recognition: Effects of Character Normalization and Feature Extraction Cheng-Lin LIU Hiroshi SAKO Hiromichi FUJISAWA Publication IEICE TRANSACTIONS on Information and Systems Vol.E88-D No.8 pp.1791-1798 Publication Date: 2005/08/01 Online ISSN: DOI: 10.1093/ietisy/e88-d.8.1791 Print ISSN: 0916-8532 Type of Manuscript: Special Section PAPER (Special Section on Document Image Understanding and Digital Documents) Category: String Recognition Keyword: numeral string recognition, integrated segmentation and recognition, normalization, aspect ratio function, feature extraction, slant correction, Full Text: PDF(715.2KB)>>
Summary: The performance of integrated segmentation and recognition of handwritten numeral strings relies on the classification accuracy and the non-character resistance of the underlying character classifier, which is variable depending on the techniques of pattern normalization, feature extraction, and classifier structure. In this paper, we evaluate the effects of 12 normalization functions and four selected feature types on numeral string recognition. Slant correction (deslant) is combined with the normalization functions and features so as to create 96 feature vectors, which are classified using two classifier structures. In experiments on numeral string images of the NIST Special Database 19, the classifiers have yielded very high string recognition accuracies. We show the superiority of moment normalization with adaptive aspect ratio mapping and the gradient direction feature, and observed that slant correction is beneficial to string recognition when combined with good normalization methods. | ||||||||||
| ||||||||||
