Abstract
Document image enhancement and binarization are commonly performed prior to document analysis and recognition tasks for improving the efficiency and accuracy of optical character recognition (OCR) systems. This is because directly recognizing text in degraded documents, particularly in color images, often results in unsatisfactory recognition performance. To address these issues, existing methods train independent generative adversarial networks (GANs) for different color channels to remove shadows and noise, which, in turn, facilitates efficient text information extraction. However, deploying multiple GANs results in long training and inference times. To reduce both training and inference times of document image enhancement and binarization models, we propose MFE-GAN, an efficient GAN-based framework with multi-scale feature extraction (MFE), which incorporates Haar wavelet transformation (HWT) and normalization to process document images before feeding them into GANs for training. In addition, we present novel generators, discriminators, and loss functions to improve the model's performance, and we conduct ablation studies to demonstrate their effectiveness. Experimental results on the Benchmark, Nabuco, and CMATERdb datasets demonstrate that the proposed MFE-GAN significantly reduces the total training and inference times while maintaining comparable performance with respect to state-of-the-art (SOTA) methods. The implementation of this work is available at this https URL.
Abstract (translated)
文档图像增强和二值化通常在文档分析和识别任务之前进行，以提高光学字符识别（OCR）系统的效率和准确性。这是因为直接从质量较差的文档中（尤其是在彩色图像中）识别文本往往会导致不理想的识别性能。为了解决这些问题，现有的方法训练独立的生成对抗网络（GANs），针对不同的颜色通道来移除阴影和噪声，这有助于高效地提取文本信息。然而，部署多个GAN会增加训练和推理的时间。 为了减少文档图像增强和二值化模型的训练及推理时间，我们提出了一种基于GAN的高效框架MFE-GAN，该框架集成了多尺度特征提取（MFE），使用Haar小波变换（HWT）和标准化处理文档图像，在输入给GAN进行训练之前对其进行预处理。此外，我们还提出了新颖的生成器、判别器和损失函数以提高模型性能，并通过消融研究证明了它们的有效性。 在Benchmark、Nabuco和CMATERdb数据集上的实验结果表明，所提出的MFE-GAN显著减少了总的训练和推理时间，同时保持与最先进的（SOTA）方法相当的性能。这项工作的实现可在以下网址获得：[此处应插入实际链接]。
URL
https://arxiv.org/abs/2512.14114
