A Language-Independent, Open-Vocabulary System Based on HMMs for Recognition of Ultra Low Resolution Words
| Type of publication: | Inproceedings |
| Citation: | eins08:sac |
| Booktitle: | 23rd Annual ACM Symposium on Applied Computing (ACM SAC 2008), Fortaleza, Ceara, Brasil |
| Year: | 2008 |
| Pages: | 429--433 |
| URL: | http://www.hennebert.org/downl... |
| DOI: | 10.1145/1363686.1363791 |
| Abstract: | In this paper, we introduce and evaluate a system capable of recognizing ultra low resolution words extracted from images such as those frequently embedded on web pages. The design of the system has been driven by the following constraints. First, the system has to recognize small font sizes where anti-aliasing and resampling procedures have been applied. Such procedures add noise on the patterns and complicate any a priori segmentation of the characters. Second, the system has to be able to recognize any words in an open vocabulary setting, potentially mixing different languages. Finally, the training procedure must be automatic, i.e. without requesting to extract, segment and label manually a large set of data. These constraints led us to an architecture based on ergodic HMMs where states are associated to the characters. We also introduce several improvements of the performance increasing the order of the emission probability estimators and including minimum and maximum duration constraints on the character models. The proposed system is evaluated on different font sizes and families, showing good robustness for sizes down to 6 points. |
| Keywords: | image analysis, OCR, Pattern Recognition |
| Authors | |
| Added by: | [] |
| Total mark: | 0 |
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