Abstract
A new approach for Stochastic Error-Correcting Language Modeling based on Weighted Finite-State Transducers (WFSTs) is proposed as a method to post-process the results of an optical character recognizer (OCR). Instead of using the recognized string as an input to the transducer, in our approach the complete set of OCR hypotheses, a sequence of vectors of a posteriori class probabilities, is used to build a WFST that is then composed with independent WFSTs for the error and language models. This combines the practical advantages of a de-coupled (OCR + post-processor) model with the full power of an integrated model.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
