Hough Encoder for Machine Readable Zone Localization

In this paper, we deal with the document machine readable zone (MRZ) detection in the images taken by smartphones. These images could have low quality and projective distortions. Current solutions for the considered task are mostly image processing algorithms. All of them have precision problems in various use cases. Known neural network methods could address this issue, but impose strict requirements for computation power which is critical for on-device inference. Therefore, we propose a method that combines neural network and image processing that is both lightweight and accurate. The network is trained to process the input images and obtain the heatmap of MRZ characters. After that, we use connected components analysis to merge the characters into lines and evaluate the MRZ bounding box. The proposed neural network is a light version of the Hough encoder—architecture that was designed to work with projectively distorted images. Our network is 1.7 times smaller than the original Hough encoder and more than 100 times smaller in comparison with typical autoencoders, which makes it possible to use the proposed method in embedded devices. Experiments were held on the open synthetic dataset of the documents with 3 types of MRZ. Our results show that our method gives high quality on the test dataset, and have less trainable parameter compared to the common solution: Unet.