A rule-based post-processing approach to improve Persian OCR performance

Optical Character Recognition (OCR) is a system to convert images including text into an editable text. Nowadays, the accuracy of these systems is acceptable in images with simple-structure and high quality. However, the performance degrades for images with complex-structure, low quality, and in the presence of noise, scratches, pictures, stamps, or other non-textual symbols. This paper proposes a Persian OCR post-processing technique to increase the accuracy of the OCR systems dealing with real-world challenging samples. The proposed method extracts five features in each line of the text and uses seven proposed rules to investigate whether that line should be ignored or not. To evaluate the proposed method, Khana (structural based) and Bina (deep learning-based) Persian OCR systems have utilized, and a dataset containing 200 complex-structure images and 100 simple-structure images have been collected. The accuracy of the Khana and Bina in images with complex-structure is 39% and 58%, respectively, while after applying the proposed post-processing method the accuracy increases to 93% and 91%, respectively.