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We describe the development of a real-time smartphone app that allows theuser to digitize paper receipts in a novel way by "waving" their phone over thereceipts and letting the app automatically detect and rectify the receipts forsubsequent text recognition.  We show that traditional computer vision algorithms for edge and cornerdetection do not robustly detect the non-linear and discontinuous edges andcorners of a typical paper receipt in real-world settings. This is particularlythe case when the colors of the receipt and background are similar, or whereother interfering rectangular objects are present. Inaccurate detection of areceipt's corner positions then results in distorted images when using anaffine projective transformation to rectify the perspective.  We propose an innovative solution to receipt corner detection by treatingeach of the four corners as a unique "object", and training a Single ShotDetection MobileNet object detection model. We use a small amount of real dataand a large amount of automatically generated synthetic data that is designedto be similar to real-world imaging scenarios.  We show that our proposed method robustly detects the four corners of areceipt, giving a receipt detection accuracy of 85.3% on real-world data,compared to only 36.9% with a traditional edge detection-based approach. Ourmethod works even when the color of the receipt is virtually indistinguishablefrom the background.  Moreover, our method is trained to detect only the corners of the centraltarget receipt and implicitly learns to ignore other receipts, and otherrectangular objects. Including synthetic data allows us to train an even bettermodel. These factors are a major advantage over traditional edgedetection-based approaches, allowing us to deliver a much better experience tothe user.

Automatic Detection and Rectification of Paper Receipts on Smartphones