Name
Abstract | ||
|---|---|---|
The aim of this work is to propose a new approach to the recognition of historical texts by providing an adaptive mechanism that automatically tunes itself to a specific book. The system is based on clustering together all the similar words in a book/text and simultaneously handling entire class. The paper describes the architecture of such a system and new algorithms that have been developed for robust word image comparison (including registration, optical flow based distortion compensation, and adaptive binarization). Results for a large dataset are presented as well. Over 23% recognition improvement is demonstrated. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/ICDAR.2009.133 | ICDAR-1 |
Keywords | Field | DocType |
historical text,entire class,historical books,specific book,recognition improvement,adaptive mechanism,new algorithm,adaptive binarization,distortion compensation,new approach,large dataset,word-based adaptive ocr,optical imaging,shape,engines,optical flow,document processing,text analysis,history,electronic publishing,optical character recognition,image recognition | Computer science,Artificial intelligence,Natural language processing,Cluster analysis,Distortion,Word processing,Computer vision,Architecture,Document processing,Optical character recognition,Speech recognition,Optical flow,Electronic publishing | Conference |
Citations | PageRank | References |
12 | 0.78 | 11 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Vladimir Kluzner | 1 | 14 | 1.82 |
| Asaf Tzadok | 2 | 26 | 2.91 |
| Yuval Shimony | 3 | 12 | 0.78 |
| Eugene Walach | 4 | 100 | 11.65 |
| Apostolos Antonacopoulos | 5 | 378 | 36.45 |