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Neural Computing and Applications
Published in: Neural Computing and Applications 6/2014

01-11-2014 | Original Article

Annotated comparisons of proposed preprocessing techniques for script recognition

Authors: Tanzila Saba, Amjad Rehman, Ayman Altameem, Mueen Uddin

Published in: Neural Computing and Applications | Issue 6/2014

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Abstract

Offline cursive script recognition and their associated issues are still fresh despite of last few decades’ research. This paper presents an annotated comparison of proposed and recently published preprocessing techniques with reported work in the offline cursive script recognition. Normally, in the offline script analysis, the input is a paper image or a word or a digit and the desired output is ASCII text. This task involves several preprocessing steps, and some of them are quite hard such as line removal from text, skew removal, reference line detection (lower/upper baselines), slant removal, scaling, noise elimination, contour smoothing and skeleton. Moreover, subsequent stage of segmentation (if any) and recognition is also highly dependent on these preprocessing techniques. This paper presents an analysis and annotated comparison of latest preprocessing techniques proposed by authors with those reported in the literature on IAM/CEDAR benchmark databases. Finally, future work and persist problems are highlighted.
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Metadata
Title
Annotated comparisons of proposed preprocessing techniques for script recognition
Authors
Tanzila Saba
Amjad Rehman
Ayman Altameem
Mueen Uddin
Publication date
01-11-2014
Publisher
Springer London
Published in
Neural Computing and Applications / Issue 6/2014
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-014-1618-9

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