178,485 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
Texture feature extraction methods: A survey
Texture analysis is used in a very broad range of fields and applications, from texture classification (e.g., for remote sensing) to segmentation (e.g., in biomedical imaging), passing through image synthesis or pattern recognition (e.g., for image inpainting). For each of these image processing procedures, first, it is necessary to extract—from raw images—meaningful features that describe the texture properties. Various feature extraction methods have been proposed in the last decades. Each of them has its advantages and limitations: performances of some of them are not modified by translation, rotation, affine, and perspective transform; others have a low computational complexity; others, again, are easy to implement; and so on. This paper provides a comprehensive survey of the texture feature extraction methods. The latter are categorized into seven classes: statistical approaches, structural approaches, transform-based approaches, model-based approaches, graph-based approaches, learning-based approaches, and entropy-based approaches. For each method in these seven classes, we present the concept, the advantages, and the drawbacks and give examples of application. This survey allows us to identify two classes of methods that, particularly, deserve attention in the future, as their performances seem interesting, but their thorough study is not performed yet
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