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Image binarization techniques are being popularly used in enhancement ofnoisy and/or degraded images catering different Document Image Anlaysis (DIA)applications like word spotting, document retrieval, and OCR. Most of theexisting techniques focus on feeding pixel images into the Convolution NeuralNetworks to accomplish document binarization, which may not produce effectiveresults when working with compressed images that need to be processed withoutfull decompression. Therefore in this research paper, the idea of documentimage binarization directly using JPEG compressed stream of document images isproposed by employing Dual Discriminator Generative Adversarial Networks(DD-GANs). Here the two discriminator networks - Global and Local work ondifferent image ratios and use focal loss as generator loss. The proposed modelhas been thoroughly tested with different versions of DIBCO dataset havingchallenges like holes, erased or smudged ink, dust, and misplaced fibres. Themodel proved to be highly robust, efficient both in terms of time and spacecomplexities, and also resulted in state-of-the-art performance in JPEGcompressed domain.

Document Image Binarization in JPEG Compressed Domain using Dual Discriminator Generative Adversarial Networks