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Title: Segmentation-based, omnifont printed Arabic character recognition without font identification
Authors: Qaroush, Aziz 
Awad, Abdalkarim 
Modallal, Mohammad 
Ziq, Malik 
Keywords: Optical character recognitions;Arabic character sets (Data processing);Mono-font;Mixed-font;Pattern recognition systems;Image segmentation;Character segmentation;Image processing - Digital techniques;Convolutional neural networks
Issue Date: 10-Oct-2020
Abstract: Optical Character Recognition OCR is an essential part of many real-world applications such as digital archiving, automatic number plate recognition, handle cheques, etc. However, developing an OCR for printed Arabic text is still a challenging and open research field due to the special characteristics of Arabic cursive script. In this paper, we propose a segmentation-based, omnifont, open-vocabulary OCR for printed Arabic text. The proposed approach doesn’t require an explicit font type recognition stage. It uses an explicit, indirect character segmentation method. The presented segmentation method is baseline dependent and employs a hybrid, three-steps character segmentation algorithm to handle the problem of character overlapping. Besides, it uses a set of topological features that are designed and generalized to make the segmentation approach font independent. The segmented characters are fed as an input to a convolutional neural network for feature extraction and recognition. The APTID-MF data set has been used for testing and evaluation. The average accuracy of the proposed segmentation stage is 95%, while the average accuracy of the recognition stage is 99.97%. The whole approach achieves an average accuracy of 95% without using font-type recognition or any post-processing techniques.
Appears in Collections:6. BZU Dataset Collection

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