A New Approach to Detect and Extract Characters from Off-Line Printed Images and Text

Characters extraction is the most critical pre-processing step for any off-line text recognition system because the characters are the smallest unit of any language script. The paper proposes an approach to segment character images from the text containing images and computer printed or handwritten words. This segmentation app roach is based on a set of properties for each connected component (object) in the whole binary image of the machine printed or handwritten text containing some other images. These words which are printed along with some images are of different lengths and are printed by different cursive fonts of different sizes. This character extraction technique is applied for the segmentation of untouched characters from the machine printed or handwritten words of varying length written on a noisy background having some images etc. Very promising results are achieved which reveals the robustness of the proposed character detection and extraction technique.