Abstract
Text line segmentation in offline handwritten documents remains a challenge because the offline handwritten text lines are often inconsistency curved and skewed. More serious is the space between lines is not enough to distinguish them. In this paper, we propose a novel offline handwritten text line segmentation method by writing pheromone diffusion and convergence. According to the principle of gravity, we apply it to the lines location of the offline handwritten texts, the pheromone diffusion and convergence can learn to generate the pheromone matrix for extracting the key locations and fragments of the text line, that is made robust to deal with various offline handwritten documents with curved and multi-skewed text lines. In experiments on a commonly used database with offline handwritten text images, our method can significantly improve upon state-of-the-art text line segmentation methods.
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Acknowledgement
This work is sponsored by the National Natural Science Fund of China (61976118, 61806098), Jiangsu Province Natural Science Foundation (BK20180142), Jiangsu Province Natural Science Foundation for Colleges and Universities (17KJB520020, 18KJB520029).
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Wang, Y., Xiao, W. (2020). Handwritten Text Line Segmentation Method by Writing Pheromone Diffusion and Convergence. In: Shen, J., Chang, YC., Su, YS., Ogata, H. (eds) Cognitive Cities. IC3 2019. Communications in Computer and Information Science, vol 1227. Springer, Singapore. https://doi.org/10.1007/978-981-15-6113-9_12
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DOI: https://doi.org/10.1007/978-981-15-6113-9_12
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