Abstract
This paper presents an HMM-based recognizer for the off-line recognition of handwritten words. Word models are the concatenation of context-dependent character models (trigraphs). The trigraph models we consider are similar to triphone models in speech recognition, where a character adapts its shape according to its adjacent characters. Due to the large number of possible context-dependent models to compute, a top-down clustering is applied on each state position of all models associated with a particular character. This clustering uses decision trees, based on rhetorical questions we designed. Decision trees have the advantage to model untrained trigraphs. Our system is shown to perform better than a baseline context independent system, and reaches an accuracy higher than 74% on the publicly available Rimes database.
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.
