基于Hough的设备上文档定位方法
Advanced Hough-based method for on-device document localization
摘要：
对设备上文档识别系统的需求随着更严格的隐私和安全要求的出现而增加。在这样的系统中，不存在从终端设备到第三方信息处理服务器的数据传输。响应时间对于设备上文档识别的用户体验至关重要。再加上智能手机等消费级终端设备上无法使用离散GPU、强大的CPU或大RAM容量，时间限制对设备上执行的应用算法的计算复杂性产生了重大限制。在这项工作中，我们考虑在图像中的文档位置，没有事先知道的文档内容或其内部结构。根据已发表的作品，至少有5个系统提供了设备上的文档定位的解决方案。所有这些系统都使用可以被认为是基于Hough的定位方法。这样的系统的精度似乎是低于国家的最先进的解决方案，其目的是不占有限的计算资源。我们提出了一种先进的Hough基方法。与其他方法相比，它考虑了中心投影模型的几何不变量，并结合边缘和颜色特征进行文档边界检测。所提出的方法允许SmartDoc数据集在精度方面的第二个最佳结果，超过了U-网络，如神经网络。在更具挑战性的MIDV-500数据集上进行评估时，与已发表的方法相比，该算法保证了最佳精度。我们的方法保留了对设备上计算的适用性。
英文摘要：
The demand for on-device document recognition systems increases in conjunction with the emergence of more strict privacy and security requirements. In such systems, there is no data transfer from the end device to a third-party information processing servers. The response time is vital to the user experience of on-device document recognition. Combined with the unavailability of discrete GPUs, powerful CPUs, or a large RAM capacity on consumer-grade end devices such as smartphones, the time limitations put significant constraints on the computational complexity of the applied algorithms for on-device execution. In this work, we consider document location in an image without prior knowledge of the document content or its internal structure. In accordance with the published works, at least 5 systems offer solutions for on-device document location. All these systems use a location method which can be considered Hough-based. The precision of such systems seems to be lower than that of the state-of-the-art solutions which were not designed to account for the limited computational resources. We propose an advanced Hough-based method. In contrast with other approaches, it accounts for the geometric invariants of the central projection model and combines both edge and color features for document boundary detection. The proposed method allowed for the second best result for SmartDoc dataset in terms of precision, surpassed by U-net like neural network. When evaluated on a more challenging MIDV-500 dataset, the proposed algorithm guaranteed the best precision compared to published methods. Our method retained the applicability to on-device computations.
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