Abstract
The field of Optical Character Recognition (OCR) has gained more attention in the recent years because of its importance and applications. Some examples of OCR are: video indexing, references archiving, car-plate recognition, and data entry. In this work a robust system for OCR is presented. The proposed system recognizes text in poor quality images. Characters are extracted from the given poor quality image to be recognized using chain-code representation. The proposed system uses Google online spelling to suggest replacements for words which are misspelled during the recognition process. For evaluating the proposed system, the born-digital dataset ICDAR is used. The proposed system achieves 74.02 % correctly recognized word rate. The results demonstrate that the proposed system recognizes text in poor quality images efficiently.
Research Department
Research Journal
Advances in Intelligent Systems and Computing
Research Member
Research Rank
3
Research Publisher
Springer
Research Vol
Vol 407
Research Website
http://link.springer.com/chapter/10.1007/978-3-319-26690-9_14
Research Year
2015
Research_Pages
151-161
Research Abstract
