On Using Entropy for Enhancing Handwriting Preprocessing

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 slantand 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.