Fast Arabic Glyph Recognizer based on Haar Cascade Classifiers
Ashraf AbdelRaouf, Colin Anthony Higgins, Tony Pridmore, Mahmoud I. Khalil · 2014
Optical Character Recognition (OCR) is an important technology. The Arabic language lacks both the variety of OCR systems and the depth of research relative to Roman scripts. A machine learning, Haar-Cascade classifier (HCC) approach was introduced by Viola and Jones (Viola and Jones 2001) to achieve rapid object detection based on a boosted cascade Haar-like features. Here, that approach is modified for the first time to suit Arabic glyph recognition. The HCC approach eliminates problematic steps in the pre-processing and recognition phases and, most importantly, the character segmentation stage. A recognizer was produced for each of the 61 Arabic glyphs that exist after the removal of diacritical marks. These recognizers were trained and tested on some 2,000 images each. The system was tested with real text images and produces a recognition rate for Arabic glyphs of 87%. The proposed method is fast, with an average document recognition time of 14.7 seconds compared with 15.8 seconds for commercial software.