TY - JOUR ID - 22016 TI - A rule-based post-processing approach to improve Persian OCR performance JO - Scientia Iranica JA - SCI LA - en SN - 1026-3098 AU - Khosrobeigi, Z. AU - Veisi, H. AU - Ahmadi, H.R. AU - Shabanian, H. AD - Faculty of New Sciences and Technologies, University of Tehran, Tehran, Iran AD - Department of Electrical and Computer Engineering, University of Memphis, Memphis, USA Y1 - 2020 PY - 2020 VL - 27 IS - 6 SP - 3019 EP - 3033 KW - Persian Optical Character Recognition KW - Post-processing KW - Complex-Structure Images DO - 10.24200/sci.2020.53435.3267 N2 - 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. UR - https://scientiairanica.sharif.edu/article_22016.html L1 - https://scientiairanica.sharif.edu/article_22016_f5a46fb93b59611f0d6bdd35a7db178a.pdf ER -