Recognizing handwritten Arabic words using grapheme segmentation and recurrent neural networks

• Published in: International journal on document analysis and recognition Vol. 17; no. 3; pp. 275 - 291
• Main Authors: Abandah, Gheith A., Jamour, Fuad T., Qaralleh, Esam A.
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 2014; Springer
• Subjects: Applied sciences; Artificial intelligence; Computer Science; Computer science; control theory; systems; Connectionism. Neural networks; Data processing. List processing. Character string processing; Exact sciences and technology; Image Processing and Computer Vision; Memory organisation. Data processing; Original Paper; Pattern Recognition; Pattern recognition. Digital image processing. Computational geometry; Software; Speech and sound recognition and synthesis. Linguistics; Recurrent neural networks; Feature evaluation and selection; Segmentation; Optical character recognition; Handwritten Arabic words; Competition; Word; Expert system; High precision; Arabic; Image matching; Holism; Linguistics; Phonetics; Selection criterion; Natural language; Skeleton; Document analysis; Pattern extraction; Execution time; Recurrent neural nets; Pattern recognition; Character recognition; Feature extraction; Manuscript character
• ISSN: 1433-2833; 1433-2825
• DOI: 10.1007/s10032-014-0218-7

The Arabic alphabet is used in around 27 languages, including Arabic, Persian, Kurdish, Urdu, and Jawi. Many researchers have developed systems for recognizing cursive handwritten Arabic words, using both holistic and segmentation-based approaches. This paper introduces a system that achieves high accuracy using efficient segmentation, feature extraction, and recurrent neural network (RNN). We describe a robust rule-based segmentation algorithm that uses special feature points identified in the word skeleton to segment the cursive words into graphemes. We show that careful selection from a wide range of features extracted during and after the segmentation stage produces a feature set that significantly reduces the label error. We demonstrate that using same RNN recognition engine, the segmentation approach with efficient feature extraction gives better results than a holistic approach that extracts features from raw pixels. We evaluated this segmentation approach against an improved version of the holistic system MDLSTM that won the ICDAR 2009 Arabic handwritten word recognition competition. On the IfN/ENIT database of handwritten Arabic words, the segmentation approach reduces the average label error by 18.5 %, the sequence error by 22.3 %, and the execution time by 31 %, relative to MDLSTM. This approach also has the best published accuracies on two IfN/ENIT test sets.