论文标题
使用双歧视器生成对抗网络在JPEG压缩域中的文档图像二进制
Document Image Binarization in JPEG Compressed Domain using Dual Discriminator Generative Adversarial Networks
论文作者
论文摘要
图像二进制技术通常用于增强嘈杂和/或退化的图像来迎合不同文档图像Anlaysis（DIA）应用（例如单词斑点，文档检索和OCR）。大多数现有技术都集中在将像素图像馈送到卷积神经网络中，以完成文档二进制化，当使用需要处理而无需完全减压的压缩图像时，这些二进制化可能不会产生有效的结果。因此，在本研究论文中，通过使用双重歧视者生成对抗网络（DD-GAN），提出了使用JPEG压缩文档图像的文档图像二进制的概念。在这里，两个歧视者网络 - 全球和本地工作在不同的图像比率上，并将焦点损失用作发生器损失。提出的模型已通过不同版本的DIBCO数据集进行了彻底的测试，该数据集具有诸如孔，擦除或弄脏的墨水，灰尘和放错地方的挑战。在时间和空间复杂性方面，该模型被证明是高度鲁棒，有效的，也导致了JPEG压缩域中的最新性能。
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.
