Text Prior Guided Scene Text Image Super-Resolution

Ma, Jianqi; Guo, Shi; Zhang, Lei

doi:10.1109/tip.2023.3237002

Cited by 42 publications

(12 citation statements)

References 48 publications

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“…PCAN [27] designs effective attention mechanisms, aiming to learn sequence‐dependent features and extract high‐frequency information. TPGSR [28] employs a text prior generator to extract categorical probability distribution as guidance for the text image reconstruction process. Text Gestalt [29] pre‐trains a text recognizer to highlight the stroke‐level details.…”

Section: Related Workmentioning

confidence: 99%

“…Inspired by the success of TSRN, many researchers have started to investigate real‐world STISR to improve the quality of LR text images, thus improving recognition accuracy. However, all of the current works concentrate on recovering LR scene text images in a fully supervised manner, that is, with all the LR‐HR pairs being used [25–29].…”

Section: Introductionmentioning

confidence: 99%

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TLWSR: Weakly supervised real‐world scene text image super‐resolution using text label

Shi

Zhu

Fang

et al. 2023

IET Image Processing

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Scene text image super-resolution (STISR) has recently received considerable attention. Existing STISR methods are applicable to the situation that all the LR-HR pairs are available. However, in real-world scenarios, it is difficult and expensive to collect ground-truth HR labels and align them with LR images, and thus it is essential to find a way to implement weakly supervised learning. We investigate the STISR problem in the situation that only a subset of HR labels is available and design a weak supervision framework using coarsegrained text labels named TLWSR, which combines incomplete supervision and inexact supervision. Specifically, a lightweight text recognition network and connectionist temporal classification loss are used to guide the super-resolution of text images during training. Extensive experiments on the benchmark TextZoom demonstrate that TLWSR generates distinguishable text images and exceeds the fully supervised baseline TSRN in boosting text recognition accuracywith only 50% HR labels available. Meanwhile, TLWSR can be applied to different super-resolution backbones and significantly improves their performance. Furthermore, TLWSR shows good generalization capability to low-quality images on scene text recognition benchmarks, which verifies the effectiveness of this framework. To the authors' knowledge, this is the first work exploring the problem of STISR in weakly supervised scenarios.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

TLWSR: Weakly supervised real‐world scene text image super‐resolution using text label

Shi

Zhu

Fang

et al. 2023

IET Image Processing

View full text Add to dashboard Cite

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“…[32] proposes a content perceptual loss to extract multi-scale text recognition features to conduct a content aware supervision. TPGSR [12], TATT [13], and C3-STISR [14] extract text-specific clues to guide the superresolution. In particular, TPGSR is the first method that additionally introduces a scene text recognizer to provide text priors.…”

Section: B Scene Text Image Super-resolutionmentioning

confidence: 99%

“…I on three major aspects: how their super-resolution blocks and loss functions are designed, and whether they use iterative super-resolution technique to boost the performance. [20] SRCNN [20] MSE × SRResNet [22] SRResNet [22] MSE × TSRN [7] SRB [7] Gradient profile loss [7] × PCAN [10] PCA [10] Edge guidance loss [10] × STT [8] TBSRN [8] Text-focused loss [8] × TPGSR [12] SRB [7] Gradient profile loss [7] ✓ TG [9] SRB [7] Stroke-focused loss [9] ×…”

Section: B Scene Text Image Super-resolutionmentioning

confidence: 99%

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Positional Information is a Strong Supervision for Volumetric Medical Image Segmentation

Yu-hong

Hou

Zeng

et al. 2023

J. Shanghai Jiaotong Univ. (Sci.)

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Scene text image super-resolution (STISR) is an important pre-processing technique for text recognition from lowresolution scene images. Nowadays, various methods have been proposed to extract text-specific information from high-resolution (HR) images to supervise STISR model training. However, due to uncontrollable factors (e.g. shooting equipment, focus, and environment) in manually photographing HR images, the quality of HR images cannot be guaranteed, which unavoidably impacts STISR performance. Observing the quality issue of HR images, in this paper we propose a novel idea to boost STISR by first enhancing the quality of HR images and then using the enhanced HR images as supervision to do STISR. Concretely, we develop a new STISR framework, called High-Resolution ENhancement (HiREN) that consists of two branches and a quality estimation module. The first branch is developed to recover the low-resolution (LR) images, and the other is an HR quality enhancement branch aiming at generating high-quality (HQ) text images based on the HR images to provide more accurate supervision to the LR images. As the degradation from HQ to HR may be diverse, and there is no pixel-level supervision for HQ image generation, we design a kernel-guided enhancement network to handle various degradation, and exploit the feedback from a recognizer and text-level annotations as weak supervision signal to train the HR enhancement branch. Then, a quality estimation module is employed to evaluate the qualities of HQ images, which are used to suppress the erroneous supervision information by weighting the loss of each image. Extensive experiments on TextZoom show that HiREN can work well with most existing STISR methods and significantly boost their performances.

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