On Using Entropy for Enhancing Handwriting Preprocessing
AbstractHandwriting 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.
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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.
Holzinger A, Stocker C, Peischl B, Simonic K-M. On Using Entropy for Enhancing Handwriting Preprocessing. Entropy. 2012; 14(11):2324-2350.Chicago/Turabian Style
Holzinger, Andreas; Stocker, Christof; Peischl, Bernhard; Simonic, Klaus-Martin. 2012. "On Using Entropy for Enhancing Handwriting Preprocessing." Entropy 14, no. 11: 2324-2350.