Treffer: Fusion of segmentation strategies for off-line cursive handwriting recognition

Title:
Fusion of segmentation strategies for off-line cursive handwriting recognition
Authors:
Verma, Brijesh., Blumenstein, Michael.
Source:
Verma, B & Blumenstein, M 2008, 'Fusion of Segmentation Strategies for Off-line Cursive Handwriting Recognition', in B Verma & B Michael (eds), Pattern Recognition Technologies and Applications Idea Group Inc (IGI),USA.
Publisher Information:
Information Science Reference
Publication Year:
2008
Collection:
Central Queensland University: aCQUIRe
Subject Terms:
Pattern perception, Applied research, Pattern perception Data processing, 890202 Application Tools and System Utilities, 080109 Pattern Recognition and Data Mining, 080108 Neural, Evolutionary and Fuzzy Computation, Handwriting recognition -- Data preprocessing -- Feature extraction -- Neural networks -- Text processing software, Book chapter
Document Type:
Buch book part
Language:
English
Relation:
Pattern recognition technologies and applications : recent advances / Brijesh Verma and Michael Blumenstein, editors. USA : Information Science Reference, 2008. Chapter 1, p. 1-16 435 pages 17 chapters 9781599048079; ACQUIRE [electronic resource] : Central Queensland University Institutional Repository.; cqu:4367; http://hdl.cqu.edu.au/10018/28510
Availability:
http://hdl.cqu.edu.au/10018/28510
Rights:
Reproduced with permission.
Accession Number:
edsbas.811D3B9B
Database:
BASE

Weitere Informationen

Cursive handwriting recognition is a challenging task for many real world applications such as document authentication, form processing, postal address recognition, reading machines for the blind, bank cheque recognition and interpretation of historical documents. Therefore, in last few decades the researchers have put enormous effort to develop various techniques for handwriting recognition. This chapter reviews existing handwriting recognition techniques and presents the current state of the art in cursive handwriting recognition. The chapter also presents segmentation strategies and a segmentation-based approach for automated recognition of unconstrained cursive handwriting. The chapter provides a comprehensive literature with basic and advanced techniques and research results in handwriting recognition for graduate students and also for advanced researchers.