Fast Glare Detection in Document Images
This addresses glare issues that hinder text recognition in mobile-captured documents, but it is incremental as it builds on existing detection techniques.
The paper tackles the problem of glare detection in document images captured by mobile devices, proposing a method that uses luminance features and binarized histograms with a convolutional neural network, achieving high recall and f-score.
Glare is a phenomenon that occurs when the scene has a reflection of a light source or has one in it. This luminescence can hide useful information from the image, making text recognition virtually impossible. In this paper, we propose an approach to detect glare in images taken by users via mobile devices. Our method divides the document into blocks and collects luminance features from the original image and black-white strokes histograms of the binarized image. Finally, glare is detected using a convolutional neural network on the aforementioned histograms and luminance features. The network consists of several feature extraction blocks, one for each type of input, and the detection block, which calculates the resulting glare heatmap based on the output of the extraction part. The proposed solution detects glare with high recall and f-score.