Detecting and extracting information from the 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, fail to extract the MRZ from 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 achieves 100% MRZ detection rate and 99.25% character recognition macro-f1 score on a passport and visa dataset.

从护照和签证上的机器可读区域 (MRZ) 检测和提取信息对于验证文件真实性变得越来越重要。然而，用于执行类似任务（例如光学字符识别）的计算机视觉方法无法以合理的准确度从护照的数字图像中提取 MRZ。我们提出了一种基于卷积神经网络的专门设计的模型，该模型能够从任意方向和大小的护照数字图像中成功提取 MRZ 信息。我们的模型在护照和签证数据集上实现了 100% 的 MRZ 检测率和 99.25% 的字符识别宏 f1 分数。