ABSTRACT
Despite several decades of research in document analysis, recognition of unconstrained handwritten documents is still considered a challenging task. Previous research in this area has shown that word recognizers produce reasonably clean output when used with a restricted lexicon. But in absence of such a restricted lexicon, the output of an unconstrained handwritten word recognizer is noisy. The objective of this research is to process noisy recognizer output and eliminate spurious recognition choices using a topic based language model. We construct a topic based language model for every document using a training data which is manually categorized. A topic categorization sub-system based on Maximum Entropy model is also trained which is used to generate the topic distribution of a test document. A given test word image is processed by the recognizer and its word recognition likelihood is refined by incorporating topic distribution of the document and topic based language model probability. The proposed method is evaluated on a publicly available IAM dataset and experimental results show significant improvement in the word recognition accuracy from 32% to 40% over a test set consisting of 4033 word images extracted from 70 handwritten document images.
- J. Perez-Cortes, J. Amerngual, J. Arlandis and R. Llobet, Stochastic error-correcting parsing for OCR postprocessing, International Conference on Pattern Recognition, 2000, pages 4405--4408, Barcelona, Spain. Google ScholarDigital Library
- F. Farooq, D. Jose and V. Govindaraju, Phrase Based Direct Model for Improving Handwriting Recognition Accuracies, To appear in International Conference on Frontiers in Handwriting Recognition, 2008, Montreal, Canada.Google Scholar
- F. Farooq, G. Chandalia and V. Govindaraju. Lexicon Reduction in Handwriting Recognition Using Topic Categorization. Under Review - In Eight International Workshop on Document Analysis Systems. Nara, Japan, 2008. Google ScholarDigital Library
- V. Govindaraju, V. Ramanaprasad, D. Lee and S. Srihari. Reading handwritten us census forms. In Proceedings of Third International Conference on Document Analysis and Recognition, pages 82--85, Montreal, Canada, 1997. Google ScholarDigital Library
- N. D. Guillevic D and Y. K. Word lexicon reduction by character spotting. Proceedings of the Seventh International Workshop on Frontiers in Handwriting Recognition. pages 373--382, 2000.Google Scholar
- S. Impedovo, P. Wang, and H. Bunke. Automatic bankcheck processing. Machine Perception and Artificial Intelligence, 28, 1997.Google Scholar
- G. Kaufmann, H. Bunke, and M. Hadorn. Lexicon reduction in an hmm-framework based on quantized feature vectors. In Proceedings of the 4th International Conference on Document Analysis and Recognition, pages 1097--1101, Washington, DC, USA, 1997. Google ScholarDigital Library
- G. Kim and V. Govindaraju. A lexicon driven approach to handwritten word recognition for real-time applications. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19(4):366--379, 1997. Google ScholarDigital Library
- G. Kim, V. Govindaraju, and S. Srihari. Architecture for handwriting recognition systems. International Journal of Document Analysis and Recognition, 2(1):37--44, 1999.Google ScholarCross Ref
- A. Koerich, R. Sabourin, and C. Suen. Large vocabulary offline handwriting recognition using a constrained level building algorithm. Pattern Analysis and Applications, 6(2):97--121, 2003.Google ScholarDigital Library
- K. Kukich, Techniques for automatically correcting words in text, ACM Computing Surveys, 24(4):377--439, 1992. Google ScholarDigital Library
- S. Madhvanath and V. Govindaraju. Holistic lexicon reduction for handwritten word recognition. In Proceedings of the SPIE - Document Recognition III, pages 224--234, San Jose, CA, 1996.Google ScholarCross Ref
- S. Madhvanath and V. Govindaraju. Syntatic methodology of pruning large lexicons in cursive script recognition. Pattern Recognition, 34(1):37--46, January 2001.Google ScholarCross Ref
- Christopher D. Manning, Prabhakar Raghavan and Hinrich Schütze, Introduction to Information Retrieval. Cambridge University Press. 2008. Google ScholarDigital Library
- U. Marti and H. Bunke. The iam-database: an english sentence database for off-line handwriting recognition. International Journal on Document Analysis and Recognition, 5:39--46, 2002.Google ScholarCross Ref
- U. Pal, P. Kundu and B. Chaudhuri, OCR error correction of an inflectional Indian language using morphological parsing, Journal of Information Science and Engineering, 16(6):903--922, 2000.Google Scholar
- N. S. R. K. Powalka and R. J. Whitrow. Word shape analysis for a hybrid recognition system. Pattern Recognition, 30(3):421--445, March 1997.Google ScholarCross Ref
- S. Srihari and E. Keubert. Integration of hand-written address interpretation technology into the united states postal service remote computer reader system. In Proceedings of Fourth International Conference on Document Analysis and Recognition, pages 892--896, Ulm, Germany, 1997. Google ScholarDigital Library
- K. Taghva and E. Stofsky. 2001. OCRSpell: an interactive spelling correction system for OCR errors in text. International Journal on Document Analysis and Recognition, 3(3):125--137.Google ScholarCross Ref
- A. Vinciarelli, S. Bengio and H. Bunke, Offline recognition of unconstrained handwritten texts using HMMs and statistical language models, IEEE transactions on Pattern analysis and Machine intelligence, 26(6):709--720, 2004 Google ScholarDigital Library
Index Terms
- Topic based language models for OCR correction
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