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Access type: UnknownSubject: search.filters.subject.Handwriting recognition

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    Item type:Publication,
    Offline handwriting identification system for Thai characters using individual change control processing
    (2017-11-03)
    Fukue, Kiyonari
    ;
    Tomokiyo, Daisuke
    ;
    Tangtisanon, Pikulkaew
    Nowadays, handwriting recognition systems has plays an important role in our life. It allows a person scribbles words on a paper and changes them to text. There are many activities such as depositing cheque that the handwriting recognition is needed. However, there are enumerable pattern that each character could be written by one person. Our paper is focused on procedure to classify which character was written by whom. To identify the writer of the character we firstly perform general steps which are segmentation, preprocessing and recognition. Then we added our new procedure, individual change control processing to gain an efficiency of individual handwriting detection.
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    Thai handwriting legal amounts recognition
    (2003-01-01)
    Chatwiriya, W.
    ;
    Klinkhachorn, P.
    ;
    Lass, N.
    A legal amount, or the word amount, is defined as the expression of a numerical value into words. It is used mostly in monetary documents. Thai legal amounts consist of a set of 17 words of 25 characters. This paper's objective is to develop a system to recognize Thai handwriting legal amounts. The strategies to improve the character recognition rates at the preprocessing and recognition process are discussed including the division of input characters into smaller subgroups: the body and high characters group, using multiple feature combinations and using the classifiers that give answers as a list of possible answers with scores. And at the post processes, we improved overall recognition rate using prior knowledge such as lexicon and syntax. We proposed the concept of partial lexicon matching mainly relying on the recognition results of the body group characters. The syntax verification algorithms are based on the sequences of word types, the joining between word types, and special rules for specific words. Irregular handwriting, i.e., touching and broken characters, are detected. Also holistic word recognition is employed to recognize these characters without explicit character segmentation.