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A neural-evolutionary approach for feature and architecture selection in online handwriting recognition

หน่วยงาน Central Queensland University, Australia

รายละเอียด

ชื่อเรื่อง : A neural-evolutionary approach for feature and architecture selection in online handwriting recognition
นักวิจัย : Verma, Brijesh. , Ghosh, Moumita.
คำค้น : 200400 Linguistics. , 200500 Literary Studies. , Pattern recognition systems. , Writing , Neural networks (Computer science)
หน่วยงาน : Central Queensland University, Australia
ผู้ร่วมงาน : -
ปีพิมพ์ : 2546
อ้างอิง : http://hdl.cqu.edu.au/10018/42774 , cqu:5528
ที่มา : Verma, B & Ghosh, M 2003, 'A neural-evolutionary approach for feature and architecture selection in online 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, 2003, 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 :

An automatic recognition of online handwritten text has been an on-going research problem for nearly four decades. It has been gaining more interest due to the increasing popularity of hand-held computers, digital notebooks and advanced cellular phones. However for these input modalities to be economical and user friendly the recognition rate should be very high for real time use. Also, the large number of writing styles and the variability between them makes the handwriting recognition problem a very challenging area for researchers. Many researchers have proposed a number of novel techniques for online handwriting recognition. However, an acceptable classification rate has not been achieved yet and there is a lack of techniques, which can find appropriate features, architecture and network parameters for online handwriting recognition. In this paper we propose a novel neurogenetic technique to improve classification accuracy through the selection of appropriate features and network parameters for online handwriting recognition. The technique incorporates an evolutionary approach for finding the most significant features, network architecture and its parameters.

บรรณานุกรม :
Verma, Brijesh. , Ghosh, Moumita. . (2546). A neural-evolutionary approach for feature and architecture selection in online handwriting recognition.
    กรุงเทพมหานคร : Central Queensland University, Australia.
Verma, Brijesh. , Ghosh, Moumita. . 2546. "A neural-evolutionary approach for feature and architecture selection in online handwriting recognition".
    กรุงเทพมหานคร : Central Queensland University, Australia.
Verma, Brijesh. , Ghosh, Moumita. . "A neural-evolutionary approach for feature and architecture selection in online handwriting recognition."
    กรุงเทพมหานคร : Central Queensland University, Australia, 2546. Print.
Verma, Brijesh. , Ghosh, Moumita. . A neural-evolutionary approach for feature and architecture selection in online handwriting recognition. กรุงเทพมหานคร : Central Queensland University, Australia; 2546.