Abstract
Identifying Tattoo is an integral part of forensic investigation and crime identification. Tattoo text detection is challenging because of its freestyle handwriting over the skin region with a variety of decorations. This paper introduces Deformable Convolution and Inception based Neural Network (DCINN) for detecting tattoo text. Before tattoo text detection, the proposed approach detects skin regions in the tattoo images based on color models. This results in skin regions containing Tattoo text, which reduces the background complexity of the tattoo text detection problem. For detecting tattoo text in the skin regions, we explore a DCINN, which generates binary maps from the final feature maps using differential binarization technique. Finally, polygonal bounding boxes are generated from the binary map for any orientation of text. Experiments on our Tattoo-Text dataset and two standard datasets of natural scene text images, namely, Total-Text, CTW1500 show that the proposed method is effective in detecting Tattoo text as well as natural scene text in the images. Furthermore, the proposed method outperforms the existing text detection methods in several criteria.
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Chowdhury, T., Shivakumara, P., Pal, U., Lu, T., Raghavendra, R., Chanda, S. (2021). DCINN: Deformable Convolution and Inception Based Neural Network for Tattoo Text Detection Through Skin Region. In: Lladós, J., Lopresti, D., Uchida, S. (eds) Document Analysis and Recognition – ICDAR 2021. ICDAR 2021. Lecture Notes in Computer Science(), vol 12822. Springer, Cham. https://doi.org/10.1007/978-3-030-86331-9_22
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