878 research outputs found
An Efficient Hidden Markov Model for Offline Handwritten Numeral Recognition
Traditionally, the performance of ocr algorithms and systems is based on the
recognition of isolated characters. When a system classifies an individual
character, its output is typically a character label or a reject marker that
corresponds to an unrecognized character. By comparing output labels with the
correct labels, the number of correct recognition, substitution errors
misrecognized characters, and rejects unrecognized characters are determined.
Nowadays, although recognition of printed isolated characters is performed with
high accuracy, recognition of handwritten characters still remains an open
problem in the research arena. The ability to identify machine printed
characters in an automated or a semi automated manner has obvious applications
in numerous fields. Since creating an algorithm with a one hundred percent
correct recognition rate is quite probably impossible in our world of noise and
different font styles, it is important to design character recognition
algorithms with these failures in mind so that when mistakes are inevitably
made, they will at least be understandable and predictable to the person
working with theComment: 6pages, 5 figure
Recognition of Cursive Arabic Handwritten Text using Embedded Training based on HMMs
In this paper we present a system for offline recognition cursive Arabic handwritten text based on Hidden Markov Models HMMs The system is analytical without explicit segmentation used embedded training to perform and enhance the character models Extraction features preceded by baseline estimation are statistical and geometric to integrate both the peculiarities of the text and the pixel distribution characteristics in the word image These features are modelled using hidden Markov models and trained by embedded training The experiments on images of the benchmark IFN ENIT database show that the proposed system improves recognitio
- …