  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.
