论文标题
使用卷积神经网络从签证和护照文件中提取MRZ代码
MRZ code extraction from visa and passport documents using convolutional neural networks
论文作者
论文摘要
从护照和签证中检测和提取机器可读区（MRZ）的信息对于验证文档的真实性变得越来越重要。但是，执行类似任务的计算机视觉方法，例如光学特征识别（OCR），无法以合理的精度提取Passport的MRZ数字图像。我们提出了一个基于卷积神经网络的特殊设计模型，该模型能够从任意方向和大小的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达到了100％MRZ检测率，并达到98.36％的字符识别宏F1分数。
Detecting and extracting information from 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 (OCR), fail to extract the MRZ given 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 achieved 100% MRZ detection rate and 98.36% character recognition macro-f1 score on a passport and visa dataset.
