Preprints

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

Preprint, 2020

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. Download paper here

Recommended citation: Yichuan Liu, Hailey James, Otkrist Gupta, Dan Raviv. "MRZ code extraction from visa and passport documents using convolutional neural networks." arXiv preprint arXiv:2009.05489 (2020). https://arxiv.org/abs/2009.05489

OCR Graph Features for Manipulation Detection in Documents

Preprint, 2020

Detecting manipulations in digital documents is becoming increasingly important for information verification purposes. Due to the proliferation of image editing software, altering key information in documents has become widely accessible. Nearly all approaches in this domain rely on a procedural approach, using carefully generated features and a hand-tuned scoring system, rather than a data-driven and generalizable approach. We frame this issue as a graph comparison problem using the character bounding boxes, and propose a model that leverages graph features using OCR (Optical Character Recognition). Our model relies on a data-driven approach to detect alterations by training a random forest classifier on the graph-based OCR features. We evaluate our algorithm’s forgery detection performance on dataset constructed from real business documents with slight forgery imperfections. Our proposed model dramatically outperforms the most closely-related document manipulation detection model on this task. Download paper here

Recommended citation: Hailey James, Otkrist Gupta, and Dan Raviv. "OCR Graph Features for Manipulation Detection in Documents." arXiv preprint arXiv:2009.05158 (2020). https://arxiv.org/abs/2009.05158