A New Symmetry-Based Transformer for Text Spotting in Person and Vehicle Re-Identification Images

Text spotting in person and vehicle re-identification images is complex due to the presence of multiple views of the same person and vehicle. Most existing models focus on text spotting in natural scene images, our work focuses on spotting in person and vehicle re-identification images. The rationale behind this work is that the person and the vehicles share symmetry properties and the bib number in the torso and license plate number in the vehicle are text. The method divides the input image into patches, and it explores vision transformation for encoding the patches into linear patches. The linearly embedded patches are fed to the feature similarity index step, which involves phase congruency and gradient magnitude to detect symmetric patches. The transformer is proposed to encode and capture textual information from the symmetry patches for text detection and recognition. The decoder receives the attention features from the encoder and fetches a multi-task head with the information about the detected and recognized text. The experiments on person and vehicle image benchmark, viz. (Person) Re-ID, RBNR, UFPR-ALPR and RodoSol datasets show significant improvement in performance when compared to other text spotting models. The effectiveness of the proposed model is validated by testing on the benchmark datasets, namely, ICDAR 2015, Total-Text and CTW1500 of natural scene images. Furthermore, cross-data validation shows the proposed method is independent of domains.