A technique for computer detection and correction of spelling errors
Communications of the ACM
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ACM Computing Surveys (CSUR)
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COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 2
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COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 2
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COLING '96 Proceedings of the 16th conference on Computational linguistics - Volume 2
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ACM Computing Surveys (CSUR)
Speech and Language Processing (2nd Edition)
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HLT '02 Proceedings of the second international conference on Human Language Technology Research
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EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
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EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 3 - Volume 3
Statistical Machine Translation
Statistical Machine Translation
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OCR (Optical Character Recognition) scanners do not always produce 100% accuracy in recognizing text documents, leading to spelling errors that make the texts hard to process further. This paper presents an investigation for the task of spell checking for OCR-scanned text documents. First, we conduct a detailed analysis on characteristics of spelling errors given by an OCR scanner. Then, we propose a fully automatic approach combining both error detection and correction phases within a unique scheme. The scheme is designed in an unsupervised & data-driven manner, suitable for resource-poor languages. Based on the evaluation on real dataset in Vietnamese language, our approach gives an acceptable performance (detection accuracy 86%, correction accuracy 71%). In addition, we also give a result analysis to show how accurate our approach can achieve.