Grayscale-Projection Based Optimal Character Segmentation for Camera-Captured Faint Text Recognition

The faint text document images possess shallow characters inherently and the camera-captured form introduces more degradations such as low-resolution, non-uniform illumination and out-of-focus blur, which make the text binarization very difficult. In this paper, we propose a grayscale-projection based optimal character segmentation method for camera-captured faint text recognition. Instead of extracting the character candidates, we use the gradient projection to extract a series of segmentation candidates which contain inter-character gaps and intra-character gaps as well. In order to select the optimal segmentation path from all possible situations, we construct a segmentation tree and set a evaluation score for each path. The score integrates the information of single point projection, overall distribution and recognition probability. Finally the optimal segmentation path is obtained by selecting the path with the highest score. We collect a faint text recognition dataset and evaluate our method on it. Experimental results show that our method outperforms the binary-projection method and the convolutional recurrent neural network approach in terms of text segmentation and recognition accuracy.