Slant Correction and Detection for Offline Cursive Handwriting using 2D Affine Transform
Cursive handwriting recognition is a machine learning field that tries to capitalize the uniqueness characteristics of character to be expressed in a series of objects of observation, then recognized by a system. There are great amounts of shape and style hand writing variation. Each writer has her/his own styles. Thus, it made difficult to perform a cursive segmentation. One of the difficulties is handwriting segmentation with a slope. In this study a slant handwriting method to detect geometry distorted images of handwriting is being proposed. The shifting processes of continued handwriting are carried out by defining the matrix of the letter through 2D affine transformation function. The image of the letter is shifted based on the slope of the words in the direction of the x or y axis. The shifting process are used a certain scaling factor. The experiments are tested using 40 continued offline handwriting. The data are obtained from IAM Database with image slope of 45 to 45 degrees. The results showed that 2D affine transformation methods are successfully detect and correct the slope of the letter and as a result avoid the over segmentation of the image candidate. It able to improve the accuracy of continued offline handwriting recognition. Keywords 2D Affine Transform; Cursive Handwriting; Offline; Slant