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SHR Neuro Cancer Cardio Lipid Metab Microb

Holzinger, A; Stocker, C; Peischl, B; Simonic, KM; .
On Using Entropy for Enhancing Handwriting Preprocessing.
ENTROPY. 2012; 14(11): 2324-2350. Doi: 10.3390/e14112324 [OPEN ACCESS]
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Leading authors Med Uni Graz: Holzinger Andreas
Co-authors Med Uni Graz: Simonic Klaus Martin
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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 degrees for the entropy-based method, compared with the +/-7.85 degrees for the alternative. On the other hand, the entropy-based skew correction yields a lower average precision of +/-2.86 degrees, compared with the average precision of +/-2.13 degrees for the alternative LSM based approach.
Find related publications in this database (Keywords): entropy; handwriting recognition; point cloud data; preprocessing