A comprehensive scheme for tattoo text detection

Tattoo text detection provides a vital clue for person and crime identification. Due to the freestyle and unconstrained nature of handwritten tattoo text over skin regions, accurate tattoo text detection is very challenging. This paper proposes a comprehensive scheme for tattoo text detection which comprises (a) adaptive Deformable Convolutional Neural Network (DCNN) for skin region detection to reduce text detection complexity (b) a Decoupled Gradient Text Detector (DGTD) for tattoo text detection from skin region (c) a Deep Q-Network (DQN) to refine the bounding boxes detected by DGTD, and (d) a Term-Frequency-Inverse-Document-Frequency (TF-IDF) model to group the words into text lines based on semantic information to fix the bounding box for the line. To test the effectiveness, the proposed method is evaluated on different datasets, namely, (i) a newly developed tattoo text dataset, (ii) benchmark bib number dataset of the marathon, and (iii) person re-identification dataset. The proposed method achieves 91.2, 87.5, and 88.8 F-scores from these three respective datasets. To demonstrate its superior performance, the text detection module (without skin detection) is also compared with state-of-the-art scene text detection methods on benchmark datasets, namely, ICDAR 2019 ArT, Total-Text, and DAST1500 and the proposed method achieves 90.3, 88.5 and 89.8 F-score from these respective datasets.