Hybrid Model of Post-Processing Techniques for Arabic OCR
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OCR is used to extract text contained in an image. One of the stages in OCR is the post-processing and it corrects the errors of OCR output text. The OCR multiple outputs approach consists of three processes: differentiation, alignment, and voting. Existing differentiation techniques suffer from the loss of important features as it uses N-versions of input images. On the other hand, alignment techniques in the literature are based on approximation while the voting process is not context-aware. These drawbacks lead to a high error rate in OCR. This research proposed three improved techniques of...
OCR is used to extract text contained in an image. One of the stages in OCR is the post-processing and it corrects the errors of OCR output text. The OCR multiple outputs approach consists of three processes: differentiation, alignment, and voting. Existing differentiation techniques suffer from the loss of important features as it uses N-versions of input images. On the other hand, alignment techniques in the literature are based on approximation while the voting process is not context-aware. These drawbacks lead to a high error rate in OCR. This research proposed three improved techniques of differentiation, alignment, and voting to overcome the identified drawbacks. These techniques were later combined into a hybrid model that can recognize the characters of the Arabic language. Each of the proposed technique was separately evaluated against three other relevant existing techniques. Experimental results showed a relative decrease in error rate on all measurements for the evaluated techniques. Similarly, the hybrid model also obtained lower WER, CER, and NWER by 30.35%, 52.42%, and 47.86% respectively when compared to the three relevant existing models.
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