Interfered-character recognition by removing interfering-lines and adjusting feature weights

Characters sometimes overlap with nontextual lines in form documents and these interfered-character would generally be recognized with poor accuracy. In this paper we propose a two-step interfering-line removing method. Positions and orientations of interfering-lines are first detected by the Hough transform. Interfering-lines widths are then determined from projection histograms. An ambiguous area is defined to bound an interfering-line. Black runs in the ambiguous are classified into four types and grouped into run-groups. The directions of hidden character strokes in each run-groups are predicted. Black pixels located in these hidden strokes are regarded as character pixels and the other black pixels are considered as interfering pixels, which will be removed then. Most OCR engines are trained by noninterfered sample characters. In order to recognize interfered-characters, we adjust the feature values by assigning a stability value to each of subregions. In our collected 1820 interfered handwritten Chinese characters, the recognition accuracy was 24.02% for interfered-characters and 89.91% for characters after removing interfering-lines