Image binarization techniques are being popularly used in enhancement of noisy and/or degraded images catering different Document Image Anlaysis (DIA) applications like word spotting, document retrieval, and OCR. Most of the existing techniques focus on feeding pixel images into the Convolution Neural Networks to accomplish document binarization, which may not produce effective results when working with compressed images that need to be processed without full decompression. Therefore in this research paper, the idea of document image binarization directly using JPEG compressed stream of document images is proposed by employing Dual Discriminator Generative Adversarial Networks (DD-GANs). Here the two discriminator networks - Global and Local work on different image ratios and use focal loss as generator loss. The proposed model has been thoroughly tested with different versions of DIBCO dataset having challenges like holes, erased or smudged ink, dust, and misplaced fibres. The model proved to be highly robust, efficient both in terms of time and space complexities, and also resulted in state-of-the-art performance in JPEG compressed domain.

翻译：现有技术大多侧重于将像素图像输入进进进进进进神经网络以完成文档的二进制,在与需要处理的压缩图像合作时,这些图像在不完全减压的情况下可能不会产生有效结果。因此,在本研究文件中,通过使用双分分解器生成反向网络(DD-GANs),直接使用JPEG压缩文件图像流,提出了直接使用JPEG压缩文件图像流的文档图像二进制概念。在这里,两个歧视者网络――全球和地方不同图像比率的工作,并将焦点损失作为发电机损失。提议的模型已经用不同版本的DIBCO数据集进行彻底测试,这些版本的挑战包括洞洞、被清除或被涂黑的墨水、灰尘和放错的纤维。事实证明,该模型在时间和空间复杂性方面都非常可靠,效率很高,还导致JEG压缩域的状态。