Computer Science > Computer Vision and Pattern Recognition
[Submitted on 10 Sep 2020 (v1), last revised 14 Sep 2020 (this version, v2)]
Title:OCR Graph Features for Manipulation Detection in Documents
View PDFAbstract: 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.
Submission history
From: Hailey Joren [view email][v1] Thu, 10 Sep 2020 21:50:45 UTC (1,146 KB)
[v2] Mon, 14 Sep 2020 15:52:09 UTC (1,149 KB)
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