In this study, we evaluate the performance of multiple state-of-the-art SR
GAN (Super Resolution Generative Adversarial Network) models, ESRGAN,
Real-ESRGAN and EDSR, on a benchmark dataset of real-world images which undergo
degradation using a pipeline. Our results show that some models seem to
significantly increase the resolution of the input images while preserving
their visual quality, this is assessed using Tesseract OCR engine. We observe
that EDSR-BASE model from huggingface outperforms the remaining candidate
models in terms of both quantitative metrics and subjective visual quality
assessments with least compute overhead. Specifically, EDSR generates images
with higher peak signal-to-noise ratio (PSNR) and structural similarity index
(SSIM) values and are seen to return high quality OCR results with Tesseract
OCR engine. These findings suggest that EDSR is a robust and effective approach
for single-image super-resolution and may be particularly well-suited for
applications where high-quality visual fidelity is critical and optimized
compute.

评估多个最新的 SR GAN 模型（超分辨率生成对抗网络），包括 ESRGAN、Real-ESRGAN 和 EDSR，在经过流程处理的现实世界图像基准数据集上的性能，结果表明某些模型能够显著提高图像分辨率同时保留其视觉质量，使用 Tesserect OCR 引擎进行评估，其中 EDSR 模型在定量指标和主观视觉质量评估上优于其他模型，具有最少的计算开销，并且生成的图像具有更高的峰值信噪比（PSNR）和结构相似性指数（SSIM）值，通过 Tesseract OCR 引擎返回高质量的 OCR 结果，这些发现表明 EDSR 是一种稳健有效的单图像超分辨率方法，特别适用于对高质量视觉保真度至关重要且经过优化计算的应用。

SR-GAN 模型的比较分析

A comparative analysis of SR-GAN models

Text Recognition is one of the challenging tasks of computer vision with
considerable practical interest. Optical character recognition (OCR) enables
different applications for automation. This project focuses on word detection
and recognition in natural images. In comparison to reading text in scanned
documents, the targeted problem is significantly more challenging. The use case
in focus facilitates the possibility to detect the text area in natural scenes
with greater accuracy because of the availability of images under constraints.
This is achieved using a camera mounted on a truck capturing likewise images
round-the-clock. The detected text area is then recognized using Tesseract OCR
engine. Even though it benefits low computational power requirements, the model
is limited to only specific use cases. This paper discusses a critical false
positive case scenario occurred while testing and elaborates the strategy used
to alleviate the problem. The project achieved a correct character recognition
rate of more than 80\%. This paper outlines the stages of development, the
major challenges and some of the interesting findings of the project.

本研究着重于自然图像中文字的检测和识别，使用基于卡车上的摄像机捕捉图像的方法，利用 Tesseract OCR 引擎来识别检测到的文字区域。研究中讨论了测试中出现的一个重要的假阳性情况并阐述了缓解该问题的策略。最终，该项目实现了超过 80％的正确字符识别率，并概述了开发阶段、主要挑战和一些有趣的发现。