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Annotated comparisons of proposed preprocessing techniques for script recognition

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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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Acknowledgments

Our deepest thanks and appreciation to the Deanship of Scientific Research at King Saud University (KSU) Riyadh Kingdom of Saudi Arabia for funding this research. We are also thankful to our colleague researchers for their assistance in neural network training and testing phases.

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Authors and Affiliations

  1. College of Computer and Information Sciences, Prince Sultan University, Riyadh, KSA

    Tanzila Saba

  2. Faculty of Computing, Universiti Teknologi Malaysia, Johor Bahru, Malaysia

    Amjad Rehman

  3. College of Applied Studies and Community Services, King Saud University, Riyadh, KSA

    Ayman Altameem

  4. MIS Department CBA, Salman bin Abdul Aziz University KSA, 165, Alkharj, 11942, KSA

    Amjad Rehman

  5. Kulliah of Information and Communication Technology, International Islamic University Malaysia, Kuala Lumpur, Malaysia

    Mueen Uddin

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  1. Tanzila Saba
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Correspondence to Amjad Rehman.

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Saba, T., Rehman, A., Altameem, A. et al. Annotated comparisons of proposed preprocessing techniques for script recognition. Neural Comput & Applic 25, 1337–1347 (2014). https://doi.org/10.1007/s00521-014-1618-9

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