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A contour code feature based segmentation for handwriting recognition

หน่วยงาน Central Queensland University, Australia

รายละเอียด

ชื่อเรื่อง : A contour code feature based segmentation for handwriting recognition
นักวิจัย : Verma, Brijesh.
คำค้น : 200400 Linguistics. , 200300 Language Studies. , Pattern recognition systems. , Writing , Neural networks (Computer science)
หน่วยงาน : Central Queensland University, Australia
ผู้ร่วมงาน : -
ปีพิมพ์ : 2546
อ้างอิง : http://hdl.cqu.edu.au/10018/42778 , cqu:5529
ที่มา : Verma, B 2003, 'A contour code feature based segmentation for handwriting recognition' in International Association for Pattern Recognition. Technical Committee 10 (eds.) Proceedings of Seventh International Conference on Document analysis and recognition. United Kingdom, 3-6 August, pp.1203 -1207.
ความเชี่ยวชาญ : -
ความสัมพันธ์ : Proceedings of Seventh International Conference on Document analysis and recognition (ICDAR'03), United Kingdom, 3-6 August, 2003. United States. : IEEE Computer Society, 2003. p.1203 -1207 5 pages Refereed 0769519601 , ACQUIRE [electronic resource] : Central Queensland University Institutional Repository.
ขอบเขตของเนื้อหา : -
บทคัดย่อ/คำอธิบาย :

The purpose of this paper is to present a novel contour code feature in conjunction with a rule based segmentation for cursive handwriting recognition. A heuristic segmentation algorithm is initially used to over segment each word. Then the prospective segmentation points are passed through the rule-based module to discard the incorrect segmentation points and include any missing segmentation points. The proposed rule-based module validates every segmentation points against closed area, average character size, left character and density. During the left char validation, a contour code feature is extracted and checked weather the left of the prospective segmentation point is a character or rubbish (non-char). The neural network used for this validation was trained on character and non-character database. Following the segmentation, the contour between correct segmentation points is passed through the feature extraction module that extracts the contour code, after which another trained neural network is used for classification. The recognized characters are grouped into words and passed to a variable length lexicon that retrieves words that has highest confidence value.

บรรณานุกรม :
Verma, Brijesh. . (2546). A contour code feature based segmentation for handwriting recognition.
    กรุงเทพมหานคร : Central Queensland University, Australia.
Verma, Brijesh. . 2546. "A contour code feature based segmentation for handwriting recognition".
    กรุงเทพมหานคร : Central Queensland University, Australia.
Verma, Brijesh. . "A contour code feature based segmentation for handwriting recognition."
    กรุงเทพมหานคร : Central Queensland University, Australia, 2546. Print.
Verma, Brijesh. . A contour code feature based segmentation for handwriting recognition. กรุงเทพมหานคร : Central Queensland University, Australia; 2546.