关键词: 钢印字符识别, 实例分割, Mask-RCNN, 感兴趣区域, 灰度跳变

Abstract: In order to realize the real-time detection of stencil characters on paper medical packaging, a stencil character recognition system based on image processing and deep learning is designed. The system first uses a variety of image processing methods to preprocess the image under the original lighting, thereby automatically extracting the region of interest in the image, and inputting it into the trained Mask-RCNN network for instance segmentation, then the pixel positions of different characters and their character values in each picture are obtained. The experimental results show that, compared with the traditional character recognition method, this method can solve the problem of insignificant gray-scale jumps in the stencil character pictures of paper medical packaging well, and accurately segment and mark the stencil characters in the picture of the paper packaging box. It has high practical value and its character recognition accuracy rate reaches 99%, which provides a new solution for the recognition and recording of stamped characters on the production line.

Key words: stencil character recognition, instance segmentation, Mask-RCNN, region of interest, gray-scale jump