• Benchmarking deep models for identity card segmentation on synthetic and real data. • Synthetic-to-real transferability of segmentation networks for identity cards. • Gap analysis across background types and image distortions in real data. • Novel line-based perspective correction method using Hough lines. • Evaluation of line-based rectification methods on datasets MIDV-500 and MIDV-2020. Segmentation and perspective correction of identity documents captured by a smartphone camera are challenging due to variable illumination, different viewing angles, motion blur, occlusions, and complex backgrounds. To address these challenges, several state-of-the-art deep learning models are evaluated for identity card segmentation, including U-Net, Mask R-CNN, Mobile U-Net, Yolov8, YOLO11, and SegFormer. These models are trained on custom synthetic images and tested on real samples to evaluate transferability from synthetic to real images. Among the evaluated models, U-Net achieves the best performance, with a Dice score of 99.90% on synthetic data and 98.65% on real images. In this work, a robust line-based perspective correction method that overcomes common limitations of edge-based and corner-based rectification approaches is introduced. The method is evaluated on the custom dataset and on the public MIDV-500 and MIDV-2020 datasets. On the custom dataset, the proposed approach reduces the average corner distance to 2.59 px on synthetic images and 4.66 px on real images.
Svobodová et al. (Wed,) studied this question.