MRZ code extraction from visa and passport documents using convolutional neural networks
This addresses document authenticity verification for border control and security applications, representing a strong specific gain but is incremental as it builds on existing computer vision methods.
The paper tackled the problem of extracting Machine-Readable Zone (MRZ) information from passport and visa documents, achieving 100% MRZ detection rate and 98.36% character recognition macro-f1 score using a convolutional neural network model.
Detecting and extracting information from 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 (OCR), fail to extract the MRZ given 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 achieved 100% MRZ detection rate and 98.36% character recognition macro-f1 score on a passport and visa dataset.