Reciprocal Feature Learning via Explicit and Implicit Tasks in Scene Text Recognition
Abstract
Text recognition is a popular topic for its broad applications. In this work, we excavate the implicit task, character counting within the traditional text recognition, without additional labor annotation cost. The implicit task plays as an auxiliary branch for complementing the sequential recognition. We design a two-branch reciprocal feature learning framework in order to adequately utilize the features from both the tasks. Through exploiting the complementary effect between explicit and implicit tasks, the feature is reliably enhanced. Extensive experiments on 7 benchmarks show the advantages of the proposed methods in both text recognition and the new-built character counting tasks. In addition, it is convenient yet effective to equip with variable networks and tasks. We offer abundant ablation studies, generalizing experiments with deeper understanding on the tasks. Code is available.
Cite
@article{arxiv.2105.06229,
title = {Reciprocal Feature Learning via Explicit and Implicit Tasks in Scene Text Recognition},
author = {Hui Jiang and Yunlu Xu and Zhanzhan Cheng and Shiliang Pu and Yi Niu and Wenqi Ren and Fei Wu and Wenming Tan},
journal= {arXiv preprint arXiv:2105.06229},
year = {2021}
}
Comments
Accepted by ICDAR 2021. Code is available at https://davar-lab.github.io/publication.html or https://github.com/hikopensource/DAVAR-Lab-OCR