文献の詳細
| 論文の言語 | 英語 |
|---|---|
| 著者 | Seiichi Uchida,Megumi Sakai,Masakazu Iwamura,Shinichiro Omachi, and Koichi Kise |
| 論文名 | Extraction of Embedded Class Information from Universal Character Pattern |
| 論文誌名 | Proc. 9th International Conference on Document Analysis and Recognition (ICDAR2007) |
| Vol. | 1 |
| ページ | pp.437-441 |
| 発表場所 | Curitiba, Brazil |
| 査読の有無 | 有 |
| 年月 | 2007年9月 |
| 要約 | This paper is concerned with a universal pattern, which is defined as a character pattern designed to have high machine-readability. This universal pattern is a character pattern printed with stripes. The cross ratio calculated from the widths of the stripes represents the character class. Thus, if the boundaries of the stripes can be detected for measuring the widths, the class can be determined without ordinary recognition process. Furthermore, since the cross ratio is invariant to projective distortions, the correct class will be still determined under those distortions. This paper describes a practical scheme to recognize this universal pattern. The proposed scheme includes a novel algorithm to detect the stripe boundaries stably even from the universal pattern image contaminated by non-uniform lighting and noise. The algorithm is realized by a combination of a dynamic programming-based optimal boundary detection and a finite state automaton which represents the property of the universal pattern. Experimental results showed the proposed scheme could recognize 99.6% of the universal pattern images which underwent heavy projective distortions and non-uniform lighting. |
- 注記
<div id="red" class="blink">This paper won IAPR/ICDAR2007 Best Paper Award</div> - 次のファイルが利用可能です．
- BibTeX用エントリー
@InProceedings{Uchida2007, author = {Seiichi Uchida and Megumi Sakai and Masakazu Iwamura and Shinichiro Omachi and Koichi Kise}, title = {Extraction of Embedded Class Information from Universal Character Pattern}, booktitle = {Proc. 9th International Conference on Document Analysis and Recognition (ICDAR2007)}, year = 2007, month = sep, volume = {1}, pages = {437--441}, location = {Curitiba, Brazil} }