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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Zramdini, A. Ingold, R. |
| Copyright Year | 1995 |
| Description | Author affiliation: Inst. of Inf., Fribourg Univ., Switzerland (Zramdini, A.) |
| Abstract | A font recognition system allowing the identification of font families, weights, slopes and sizes with an accuracy of 99% for weights and slopes and 96% for families and sizes, has been developed. Our system uses a knowledge base of 240 fonts models, which have been created from a training set of text images written with these different fonts. In this paper, a study of image degradations effects on the system performances, is presented. The evaluation that has been carried out, shows that the system is robust against natural degradations such as those introduced by scanning and photocopying, but its performances decrease with very degraded document images. In order to avoid this weakness, a degradation modeling strategy has been adopted, allowing an automatic adaptation of the system to these degradations. The adaptation is derived from statistical analysis of features behavior against degradations and is performed by specific transformations applied to the system knowledge base. Some promising results are reported. |
| Starting Page | 740 |
| Ending Page | 743 |
| File Size | 433963 |
| Page Count | 4 |
| File Format | |
| ISBN | 0818671289 |
| DOI | 10.1109/ICDAR.1995.602008 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 1995-08-14 |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Degradation Image recognition Optical character recognition software Character recognition Robustness Text recognition Laboratories Feature extraction Informatics Adaptation model |
| Content Type | Text |
| Resource Type | Article |
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