IEEE Transactions on Pattern Analysis and Machine Intelligence
Two Different Approaches for Cost-Efficient Viterbi Parsing with Error Correction
SSPR '96 Proceedings of the 6th International Workshop on Advances in Structural and Syntactical Pattern Recognition
Orthographic Errors in Web Pages: Toward Cleaner Web Corpora
Computational Linguistics
Learning finite-state models for machine translation
Machine Learning
Statistical approaches to computer-assisted translation
Computational Linguistics
A unified approach for determining the underlying causes of non-stationary disturbances
International Journal of Computer Applications in Technology
Efficient OCR post-processing combining language, hypothesis and error models
SSPR&SPR'10 Proceedings of the 2010 joint IAPR international conference on Structural, syntactic, and statistical pattern recognition
Rejection threshold estimation for an unknown language model in an OCR task
SSPR&SPR'10 Proceedings of the 2010 joint IAPR international conference on Structural, syntactic, and statistical pattern recognition
Multi-modal computer assisted speech transcription
International Conference on Multimodal Interfaces and the Workshop on Machine Learning for Multimodal Interaction
Enhancing trie-based syntactic pattern recognition using AI heuristic search strategies
ICAPR'05 Proceedings of the Third international conference on Advances in Pattern Recognition - Volume Part I
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The problem of Error-Correcting Parsing (ECP) using an insertion-deletion-substitution error model and a Finite State Machine is examined. The Viterbi algorithm can be straightforwardly extended to perform ECP, though the resulting computational complexity can become prohibitive for many applications. We propose three approaches in order to achieve an efficient implementation of Viterbi-like ECP which are compatible with Beam Search acceleration techniques. Language processing and shape recognition experiments which assess the performance of the proposed algorithms are presented.