Abstract
In this paper an off-line script recognition system is described, which makes use of a language model, that consists of backoff character n-grams. The performance of this open vocabulary recognition is compared with the use of closed dictionaries. The system is based on Hidden Markov Models (HMMs) using a hybrid modeling technique, which depends on a neural vector quantizer The presented recognition results refer to the SEDAL-database of degraded English documents such as photocopy, or fax and a writer-dependent handwritten database of cursive German script samples. Our resulting system for character recognition yields significantly, better recognition results for an unlimited vocabulary using language models.
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.
