Abstract
In this paper, we propose a method for detecting handwritten ancient texts. The challenges in detecting this type of data are: the complexity of the layout of handwritten ancient texts, the varying text sizes, mixed arrangement of pictures and texts, the high number of hand-drawn patterns and the high background noise. Unlike general scene text detection tasks (ICDAR, TotalText, etc.), the texts in the images of ancient books are more densely distributed. For the features of the dataset, we propose a detection model based on cascade feature fusion called DFCOS, which aims to improve the fusion of localization information in lower layers. Specifically, bottom-up paths are created to use more localization signals from low-levels, and we incorporate skip connections to better extract information in the backbone, and then improve our model by parallel cascading. We verified the effectiveness of our DFCOS on HWAD (Handwritten Ancient-Books Dataset), a dataset containing four languages - Yi, Chinese, Tibetan and Tangut - provided by the Institute of Yi of Guizhou University of Engineering Science and National Digital Library of China, and its precision, recall and F-measure outperformed most of the existing text detection models.
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Feng, R., Zhao, F., Chen, S., Zhang, S., Wang, D. (2021). A Handwritten Text Detection Model Based on Cascade Feature Fusion Network Improved by FCOS. In: Barney Smith, E.H., Pal, U. (eds) Document Analysis and Recognition – ICDAR 2021 Workshops. ICDAR 2021. Lecture Notes in Computer Science(), vol 12917. Springer, Cham. https://doi.org/10.1007/978-3-030-86159-9_4
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DOI: https://doi.org/10.1007/978-3-030-86159-9_4
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