Large-Scale Font Identification from Document Images

Abstract

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.

Farsi Font Detection using the Adaptive RKEM-SURF Algorithm

“…Classical evaluation criteria including the recognition rate (matching accuracy) and recall according to (7)(8) are used to evaluate the effectiveness of the font detection methods [32].…”

“…Recall TP P  (8) In (7)(8), TP is the number of correct matches, P is the number of correspondences and m is the total number of matches. If the accuracy and recall are high, the performance of the system is acceptable.…”

“…Font detection is the process through which text language and font type, size and style could be identified. Although many methods can be found in the literature for font detection, most of them are composed of two major stages, i.e., feature extraction and font recognition [6,7]. In general, feature extraction phase of font detection methods is categorized into two approaches including typographical and textural features [8].…”