ShaDocFormer: A Shadow-Attentive Threshold Detector With Cascaded Fusion Refiner for Document Shadow Removal

TL;DR

ShaDocFormer is a Transformer-based model that effectively detects and removes shadows from document images captured by mobile devices, improving readability through a novel shadow detection and image restoration approach.

Contribution

It introduces ShaDocFormer, combining a shadow-attentive threshold detector and a cascaded fusion refiner for accurate shadow mask detection and image restoration.

Findings

Outperforms state-of-the-art methods in shadow removal quality

Achieves higher accuracy in shadow mask detection

Demonstrates effective handling of illumination variations

Abstract

Document shadow is a common issue that arises when capturing documents using mobile devices, which significantly impacts readability. Current methods encounter various challenges, including inaccurate detection of shadow masks and estimation of illumination. In this paper, we propose ShaDocFormer, a Transformer-based architecture that integrates traditional methodologies and deep learning techniques to tackle the problem of document shadow removal. The ShaDocFormer architecture comprises two components: the Shadow-attentive Threshold Detector (STD) and the Cascaded Fusion Refiner (CFR). The STD module employs a traditional thresholding technique and leverages the attention mechanism of the Transformer to gather global information, thereby enabling precise detection of shadow masks. The cascaded and aggregative structure of the CFR module facilitates a coarse-to-fine restoration process for the entire image. As a result, ShaDocFormer excels in accurately detecting and capturing variations in both shadow and illumination, thereby enabling effective removal of shadows. Extensive experiments demonstrate that ShaDocFormer outperforms current state-of-the-art methods in both qualitative and quantitative measurements.