Automatic travel pattern extraction from visa page stamps using CNN models

TL;DR

This paper presents an automated system using CNN models to extract travel patterns from visa stamps, significantly speeding up border crossing inspections by processing scanned visa pages with a multi-stage pipeline.

Contribution

The paper introduces a novel CNN-based pipeline for automatic visa stamp analysis, including detection and recognition of stamps, countries, dates, and entry/exit status, trained on real and synthetic data.

Findings

Effective CNN models for stamp detection and recognition

Significant speed-up in travel pattern extraction

Successful integration into a prototype tool

Abstract

Manual travel pattern inference from visa page stamps is a time consuming activity and constitutes an important bottleneck in the efficiency of traveler inspection at border crossings. Despite efforts to digitize and record the border crossing information into databases, travel pattern inference from stamps will remain a problem until every country in the world is incorporated into such a unified system. This could take decades. We propose an automated document analysis system that processes scanned visa pages and automatically extracts the travel pattern from detected stamps. The system processes the page via the following pipeline: stamp detection in the visa page; general stamp country and entry/exit recognition; Schengen area stamp country and entry/exit recognition; Schengen area stamp date extraction. For each stage of the proposed pipeline we construct neural network models and train then on a mixture of real and synthetic data. We integrated Schengen area stamp detection and date, country, entry/exit recognition models together with a graphical user interface into a prototype of an automatic travel pattern extraction tool. We find that by combining simple neural network models into our proposed pipeline a useful tool can be created which can speed up the travel pattern extraction significantly.