关键词: 字符分割, 亮度不均匀图像, 分割效果评价函数, 迭代闭环反馈, 广义学习矢量量化算法

Abstract:

Character recognition accuracy is directly affected by character segmentation. However, in the process of pressed characters in low quality images with uneven brightness, due to the uneven distribution of image brightness and the low contrast between the target characters and the background region, the accurate segmentation of pressed characters is difficult. The traditional character segmentation methods based on the binary images are difficult to determine the optimal image-binary threshold when dealing with the characters in the above situation. Therefore, an iterative closed-loop feedback segmentation method based on the evaluation function of segmentation effect is proposed. By using the evaluation function to evaluate the segmentation effect, it can judge whether to repartition or not. In addition, to improve the efficiency and accuracy of segmentation, weighted smoothing filter is used to remove the distorted waveforms in the grayscale waveforms. The generalized learning vector quantization algorithm is used to determine the optimal filter weight factor. Then, the waveform variation trend is analyzed to determine the character segmentation position. Experimental results show that the segmentation accuracy of the algorithm can reach 98.5%.

Key words: characters segmentation, uneven brightness images, segmentation effect evaluation function, iterative closed-loop feedback, generalized learning vector quantization algorithm