4,294 research outputs found
Handwritten Character Recognition of South Indian Scripts: A Review
Handwritten character recognition is always a frontier area of research in
the field of pattern recognition and image processing and there is a large
demand for OCR on hand written documents. Even though, sufficient studies have
performed in foreign scripts like Chinese, Japanese and Arabic characters, only
a very few work can be traced for handwritten character recognition of Indian
scripts especially for the South Indian scripts. This paper provides an
overview of offline handwritten character recognition in South Indian Scripts,
namely Malayalam, Tamil, Kannada and Telungu.Comment: Paper presented on the "National Conference on Indian Language
Computing", Kochi, February 19-20, 2011. 6 pages, 5 figure
Diagonal Based Feature Extraction for Handwritten Alphabets Recognition System using Neural Network
An off-line handwritten alphabetical character recognition system using
multilayer feed forward neural network is described in the paper. A new method,
called, diagonal based feature extraction is introduced for extracting the
features of the handwritten alphabets. Fifty data sets, each containing 26
alphabets written by various people, are used for training the neural network
and 570 different handwritten alphabetical characters are used for testing. The
proposed recognition system performs quite well yielding higher levels of
recognition accuracy compared to the systems employing the conventional
horizontal and vertical methods of feature extraction. This system will be
suitable for converting handwritten documents into structural text form and
recognizing handwritten names
RAPID ANALYTICAL VERIFICATION OF HANDWRITTEN ALPHANUMERIC ADDRESS FIELDS
Microsoft, Motorola, Siemens, Hitachi, IAPR, NICI, IUF
This paper presents a combination of fuzzy system and dynamic analytical model to deal with imprecise data derived from feature extraction in handwritten address images which are compared against postulated addresses for address verification. A dynamic buildingÂnumber locator is able to locate and recognise the buildingÂnumber, without knowing exactly where the buildingÂnumber starts in the candidate address line. The overall system achieved a correct sorting rate of 72.9%, 27.1% rejection rate and 0.0% error rate on a blind test set of 450 cursive handwritten addresses.
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