LDRNet: Enabling Real-Time Document Localization on Mobile Devices

Modern online services often require mobile devices to convert paper-based information into its digital counterpart, e.g., passport, ownership documents, etc. This process relies on Document Localization (DL) technology to detect the outline of a document within a photograph. In recent years, increased demand for real-time DL in live video has emerged, especially in financial services. However, existing machinelearning approaches to DL cannot be easily applied due to the large size of the underlying models and the associated long inference time. In this paper, we propose a lightweight DL model, LDRNet, to localize documents in real-time video captured on mobile devices. On the basis of a lightweight backbone neural network, we design three prediction branches for LDRNet: (1) corner points prediction; (2) line borders prediction and (3) document classification. To improve the accuracy, we design novel supplementary targets, the equal-division points, and use a new loss function named Line Loss. We compare the performance of LDRNet with other popular approaches on localization for general documents in a number of datasets. The experimental results show that LDRNet takes significantly less inference time, while still achieving comparable accuracy.