A Coarse to Fine Skew Estimation Technique for Handwritten Words

The estimation and correction of handwritten word skew is a difficult and challenging task since it has to be independent of the variations due to handwriting style and writing conditions. In this paper, a coarse-to-fine technique that integrates core-region information is presented. At first, a rough estimation and correction of the skew is accomplished by cutting vertically the word in two overlapping parts, detecting the center of mass in each part and calculating the inclination of the line that connects the two centers of mass. Afterwards, the core-region of the word is detected, the word is cut again in two overlapping parts and the centers of mass are calculated disregarding all the information outside the core-region (ascenders and descenders). The inclination of the line that connects the updated centers of mass corresponds to a finer estimation of the word skew. After correcting the detected skew the last step of core-region detection and skew correction is repeated iteratively in order to reach a finer word skew estimation that will contribute to a successful handwritten word recognition system. Extensive testing based on various test-sets has demonstrated that the proposed method outperforms the state-of-the-art algorithms concerning word skew estimation while it is more robust in variations of the writing style.