A Modular Privacy-Preserving Framework for Travel Document Segmentation and Information Extraction Using Synthetic Data

Automated travel document recognition is a key technology for digital identity verification. However, robust extraction of structured information from images captured in unconstrained conditions remains challenging due to perspective distortion, background clutter, motion blur, and heterogeneous lighting that often degrade the performance of the systems. The paper proposes a modular pipeline for automated travel document segmentation and data extraction that integrates instance segmentation, perspective rectification, optical character recognition, and rule-based field parsing. In order to avoid the use of sensitive personal data, the segmentation model is trained exclusively on a synthetic dataset generated in Blender that comprises 2500 annotated images with diverse variations in lighting, viewpoint, blur, and background. The experimental results demonstrate strong generalization from synthetic to real data with 99.50% mAP50, 99.22% mAP50-95, 90% character-level Optical Character Recognition (OCR), and 90% MRZ field extraction accuracy on synthetic data, and 88% MRZ extraction accuracy on a dataset with real documents.