Line Segmentation of Tibetan Ancient Books Based on A* Algorithm

Abstract

Line segmentation is an important step in image character recognition. However, due to the problems of interline adhesion, overlapping, and skewing of document images, the effect of text line segmentation is not ideal. Therefore, further research on image line segmentation of Tibetan ancient books is urgently needed. The standard A* algorithm is not ideal for line segmentation of Tibetan ancient books, so this paper proposes a block-based A* algorithm for line segmentation of Tibetan ancient books. The method firstly preprocesses the image such as binarization and tilt correction and then performs horizontal projection. The obtained horizontal projection histogram is subjected to smoothing and peak detection to determine the position and number of text lines. Use the peak value to find the core area of the image text, extract the upper vowel and perform line attribution. Then, the image after removing the upper vowel is divided into 7 blocks, and each block is processed separately by the A* algorithm. When the algorithm finds the text line segmentation path, five cost functions are selected to calculate the cost from the starting point to the endpoint of the line segmentation, and the minimum cost path is found for text line segmentation. Finally, merge the segmented text lines, attribute the upper vowels, and rotate the text lines to obtain the final text line segmentation result. Experimental results show that our methos can effectively deal with the problems of text line adhesion and overlap, and achieve a good effect on text line segmentation.