Abstract
Presents a 2D stochastic method for the recognition of unconstrained handwritten words in a small lexicon. The method is based on an efficient combination of hidden Markov models (HMMs) and causal Markov random fields (MRFs). It operates in a holistic manner, at the pixel level, on scaled binary word images which are assumed to be random field realizations. The state-related random fields act as smooth local estimators of specific writing strokes by merging conditional pixel probabilities along the columns of the image. The HMM component of our model provides an optimal switching mechanism between sets of MRF distributions in order to dynamically adapt to the features encountered during the left-to-right image scan. Experiments performed on a French omni-scriptor, an omni-bank database of handwritten legal check amounts provided by the A2iA company, are described in great extent.
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.
