  Handling various objects with different colors is a significant challenge for
image colorization techniques. Thus, for complex real-world scenes, the
existing image colorization algorithms often fail to maintain color
consistency. In this work, we attempt to integrate textual descriptions as an
auxiliary condition, along with the grayscale image that is to be colorized, to
improve the fidelity of the colorization process. To do so, we have proposed a
deep network that takes two inputs (grayscale image and the respective encoded
text description) and tries to predict the relevant color components. Also, we
have predicted each object in the image and have colorized them with their
individual description to incorporate their specific attributes in the
colorization process. After that, a fusion model fuses all the image objects
(segments) to generate the final colorized image. As the respective textual
descriptions contain color information of the objects present in the image,
text encoding helps to improve the overall quality of predicted colors. In
terms of performance, the proposed method outperforms existing colorization
techniques in terms of LPIPS, PSNR and SSIM metrics.
