A recognition method of machine-printed monetary amounts based on the two-dimensional segmentation and the bottom-up parsing

Abstract

We propose a new method to recognize the machine-printed monetary amount based on two-dimensional segmentation and bottom-up parsing. In conventional segmentation-based methods, the system segments the image only along the direction of the character line. This new method segments the image both horizontally and vertically, and extracts candidates of character segments correctly if there are many noises or characters are fragmented. A parsing module detects the optimal sequence of candidate segments using linguistic knowledge. In our method, a context-free grammar describes the linguistic constraints in the monetary amounts. We devised a new bottom-up parsing technique that interprets the results of character classification of the two-dimensionally segmented sub-images. We tested the validity of the new method using 1,314 images, and found that it improves the recognition accuracy significantly.