C3-STISR: Scene Text Image Super-resolution with Triple Clues

• URL: http://arxiv.org/abs/2204.14044v1
• Date: Fri, 29 Apr 2022 12:39:51 GMT
• Title: C3-STISR: Scene Text Image Super-resolution with Triple Clues
• Authors: Minyi Zhao, Miao Wang, Fan Bai, Bingjia Li, Jie Wang, Shuigeng Zhou
• Abstract summary: Scene text image super-resolution (STISR) has been regarded as an important pre-processing task for text recognition. Most recent approaches use the recognizer's feedback as clues to guide super-resolution. We present a novel method C3-STISR that jointly exploits the recognizer's feedback, visual and linguistical information as clues to guide super-resolution.
• Score: 22.41802601665541
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Scene text image super-resolution (STISR) has been regarded as an important pre-processing task for text recognition from low-resolution scene text images. Most recent approaches use the recognizer's feedback as clues to guide super-resolution. However, directly using recognition clue has two problems: 1) Compatibility. It is in the form of probability distribution, has an obvious modal gap with STISR - a pixel-level task; 2) Inaccuracy. it usually contains wrong information, thus will mislead the main task and degrade super-resolution performance. In this paper, we present a novel method C3-STISR that jointly exploits the recognizer's feedback, visual and linguistical information as clues to guide super-resolution. Here, visual clue is from the images of texts predicted by the recognizer, which is informative and more compatible with the STISR task; while linguistical clue is generated by a pre-trained character-level language model, which is able to correct the predicted texts. We design effective extraction and fusion mechanisms for the triple cross-modal clues to generate a comprehensive and unified guidance for super-resolution. Extensive experiments on TextZoom show that C3-STISR outperforms the SOTA methods in fidelity and recognition performance. Code is available in https://github.com/zhaominyiz/C3-STISR.

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