HP_DocPres: a method for classifying printed and handwritten texts in doctor's prescription

摘要

Optical Character Recognition (OCR) system is used to convert the document images, either printed or handwritten, into its electronic counterpart. But dealing with handwritten texts is much more challenging than printed ones due to the erratic writing style of the individuals. The problem becomes more severe when the input image is a doctor's prescription. Before feeding such an image to the OCR engine, the classification of printed and handwritten texts is a necessity as a doctor's prescription contains both handwritten and printed texts which are to be processed separately. Much work has been done in the domain of handwritten and printed text separation albeit work related to doctor's handwriting. In this paper, a method is proposed which first localizes the position of texts in a doctor's prescription, and then separates out the printed texts from the handwritten ones. Due to the unavailability of a large database, we have used some standard data (image) augmentation techniques to evaluate as well as to prove the robustness of our method. Besides, we have also designed a Graphical User Interface (GUI) so that anybody can visualize the output by providing a prescription image as input.