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On the Smoothed Minimum Error Entropy Criterion
Entropy 2012, 14(11), 2324-2350; doi:10.3390/e14112324
Article

On Using Entropy for Enhancing Handwriting Preprocessing

1,* , 1
,
2
 and
1
1 Research Unit Human-Computer Interaction, Institute for Medical Informatics, Statistics and Documentation, Medical University Graz, Auenbruggerplatz 2/V, A-8036 Graz, Austria 2 Softnet Austria, Infeldgasse 16b, A-8010 Graz, Austria
* Author to whom correspondence should be addressed.
Received: 21 July 2012 / Revised: 7 November 2012 / Accepted: 13 November 2012 / Published: 19 November 2012
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Abstract

Handwriting is an important modality for Human-Computer Interaction. For medical professionals, handwriting is (still) the preferred natural method of documentation. Handwriting recognition has long been a primary research area in Computer Science. With the tremendous ubiquity of smartphones, along with the renaissance of the stylus, handwriting recognition has become a new impetus. However, recognition rates are still not 100% perfect, and researchers still are constantly improving handwriting algorithms. In this paper we evaluate the performance of entropy based slant- and skew-correction, and compare the results to other methods. We selected 3700 words of 23 writers out of the Unipen-ICROW-03 benchmark set, which we annotated with their associated error angles by hand. Our results show that the entropy-based slant correction method outperforms a window based approach with an average precision of ±6.02° for the entropy-based method, compared with the ±7.85° for the alternative. On the other hand, the entropy-based skew correction yields a lower average precision of ±2:86°, compared with the average precision of ±2.13° for the alternative LSM based approach.
Keywords: entropy; handwriting recognition; point cloud data; preprocessing entropy; handwriting recognition; point cloud data; preprocessing
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).
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Holzinger, A.; Stocker, C.; Peischl, B.; Simonic, K.-M. On Using Entropy for Enhancing Handwriting Preprocessing. Entropy 2012, 14, 2324-2350.

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