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Automatic Detection and Rectification of Paper Receipts on Smartphones

2023-03-10 08:04:16
Edward Whittaker, Masashi Tanaka, Ikuo Kitagishi

Abstract

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

Abstract (translated)

我们描述了实时智能手机应用程序的开发,该应用程序以一种新颖的方式是将纸质发票的数字化,“挥手”向发票并让应用程序自动检测和纠正发票的位置,以进行后续文本识别。我们展示了传统的计算机视觉算法用于边缘和角落检测在现实世界场景中无法 robustly 检测到典型的纸质发票的非线性和离散的边缘和角落。这种情况尤其发生在发票和背景颜色相似,或者存在其他干扰的矩形物体的情况下。不准确的检测发票角落位置会导致使用阿法图新投影变换器纠正视角时产生扭曲的图像。我们提出了一种创新的解决方案,将每个角作为一个独特的“对象”,并训练一个单发检测的移动网络对象检测模型。我们使用少量的真实数据和大量的自动生成的模拟数据,设计为与现实世界图像场景相似。我们展示了我们提出的方法 robustly 检测到发票的四个角落,在现实世界数据上获得了85.3%的发票检测精度,相比之下,传统的边缘检测方法只有36.9%。我们的方法和即使发票的颜色几乎与背景相同也有效。此外,我们的方法是训练仅检测中心目标发票的四个角落,并 implicit 地学习忽略其他发票和矩形物体。包括模拟数据使我们能够训练更好的模型。这些因素是传统边缘检测方法的主要优势,使我们能够为用户提供更好的体验。

URL

https://arxiv.org/abs/2303.05763

PDF

https://arxiv.org/pdf/2303.05763.pdf


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