Abstract
AbstractThe recognition of connected characters in cursive handwriting is a difficult task with ordinary pattern‐matching techniques, since the shape of the individual character is affected by its preceding and succeeding characters. One of the authors has proposed a neural network model called the selective attention model, which has the ability to recognize and extract individual patterns from a composite of a number of elementary patterns. However, when a large number of patterns are presented concurrently, this model doe not always work correctly. In this paper, we extend the idea of this model and construct a new system, which has the ability to recognize the connected characters in cursive handwriting. it has been verified by computer simulation that this system can be used to correctly recognize connected characters.
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.
