An Ultra-lightweight Approach for Machine Readable Zone Detection via Semantic Segmentation and Fast Hough Transform

The Machine Readable Zone (MRZ) detection task is crucial for automated document processing systems, particularly in identity verification and authentication tasks. Due to the limited memory capacity of embedded devices, modern deep learning models for MRZ localization should not only show solid quality on challenging images but also have a small size. In this paper, we present HED-MRZ (Hough Encoder for Detection) - an ultra-lightweight deep learning model for MRZ detection based on semantic segmentation with direct and transposed Fast Hough Transform (FHT) layers. The usage of FHT layers allows us to deal with the global receptive field on the first layers of the network that helps to reduce not only the depth of the network but also the number of trainable parameters. Compared to the regression-based state-of-the-art YOLO-MRZ approach, HED-MRZ decreases number of undetected MRZs on the MIDV-LAIT by 50%, as well as outperforms it on other challenging datasets such as MIDV-2019 and MIDV-2020. At the same time, it has an order of magnitude fewer trainable parameters and weights only 120KB, thereby making it an ideal solution for use on embedded devices.