Medizinische Universität Graz Austria/Österreich - Forschungsportal - Medical University of Graz
Gewählte Publikation:
SHR Neuro Krebs Kardio Lipid Stoffw 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
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- Führende Autor*innen der Med Uni Graz
- Holzinger Andreas
- Co-Autor*innen der 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
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