MRZ code extraction from visa and passport documents using convolutional neural networks

Yichuan Liu, Hailey James, Otkrist Gupta, Dan Raviv

Research output: Contribution to journalArticlepeer-review

Abstract

Detecting and extracting information from the machine-readable zone (MRZ) on passports and visas is becoming increasingly important for verifying document authenticity. However, computer vision methods for performing similar tasks, such as optical character recognition, fail to extract the MRZ from digital images of passports with reasonable accuracy. We present a specially designed model based on convolutional neural networks that is able to successfully extract MRZ information from digital images of passports of arbitrary orientation and size. Our model achieves 100% MRZ detection rate and 99.25% character recognition macro-f1 score on a passport and visa dataset.

Original languageEnglish
Pages (from-to)29-39
Number of pages11
JournalInternational Journal on Document Analysis and Recognition
Volume25
Issue number1
DOIs
StatePublished - Mar 2022
Externally publishedYes

Keywords

  • Convolutional neural network
  • End-to-end recognition
  • MRZ
  • OCR
  • Object detection

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