In the last decade, the blossom of deep learning has witnessed the rapid
development of scene text recognition. However, the recognition of
low-resolution scene text images remains a challenge. Even though some
super-resolution methods have been proposed to tackle this problem, they
usually treat text images as general images while ignoring the fact that the
visual quality of strokes (the atomic unit of text) plays an essential role for
text recognition. According to Gestalt Psychology, humans are capable of
composing parts of details into the most similar objects guided by prior
knowledge. Likewise, when humans observe a low-resolution text image, they will
inherently use partial stroke-level details to recover the appearance of
holistic characters. Inspired by Gestalt Psychology, we put forward a
Stroke-Aware Scene Text Image Super-Resolution method containing a
Stroke-Focused Module (SFM) to concentrate on stroke-level internal structures
of characters in text images. Specifically, we attempt to design rules for
decomposing English characters and digits at stroke-level, then pre-train a
text recognizer to provide stroke-level attention maps as positional clues with
the purpose of controlling the consistency between the generated
super-resolution image and high-resolution ground truth. The extensive
experimental results validate that the proposed method can indeed generate more
distinguishable images on TextZoom and manually constructed Chinese character
dataset Degraded-IC13. Furthermore, since the proposed SFM is only used to
provide stroke-level guidance when training, it will not bring any time
overhead during the test phase. Code is available at
this https URL

本文提出了一种 Stroke-Aware Scene Text Image Super-Resolution 方法，通过设计规则分解英文字符和数字，设计 Stroke-Focused Module（SFM）以集中于字符的笔画级内部结构，旨在通过预训练文本识别器为位置提供笔画级别的注意力图，并控制所生成的超分辨率图像与高分辨率真实值之间的一致性，从而实现低分辨率场景文本图像识别的目的。

Text Gestalt: 基于笔画的场景文本图像超分辨率

Text Gestalt: Stroke-Aware Scene Text Image Super-Resolution

Optical Character Recognition (OCR) systems have been widely used in various
of application scenarios. Designing an OCR system is still a challenging task.
In previous work, we proposed a practical ultra lightweight OCR system (PP-OCR)
to balance the accuracy against the efficiency. In order to improve the
accuracy of PP-OCR and keep high efficiency, in this paper, we propose a more
robust OCR system, i.e. PP-OCRv2. We introduce bag of tricks to train a better
text detector and a better text recognizer, which include Collaborative Mutual
Learning (CML), CopyPaste, Lightweight CPUNetwork (LCNet), Unified-Deep Mutual
Learning (U-DML) and Enhanced CTCLoss. Experiments on real data show that the
precision of PP-OCRv2 is 7% higher than PP-OCR under the same inference cost.
It is also comparable to the server models of the PP-OCR which uses ResNet
series as backbones. All of the above mentioned models are open-sourced and the
code is available in the GitHub repository PaddleOCR which is powered by
PaddlePaddle.

本研究提出了一种更强健的 OCR 系统 PP-OCRv2，采用协作式互相学习、拷贝粘贴、深度神经网络、CTCLoss 等技巧来训练更好的文本探测器和识别器。实验表明，在相同推断成本下，PP-OCRv2 的精度比 PP-OCR 高 7%。