Abstract
In this work, we study the problem of recognizing identification (ID) information from unconstrained real-world images of ID card, which has extensively applied in practical scenarios. Nonetheless, manual ways of processing the task are impractical due to the unaffordable cost of labor and time consumption as well as the unreliable quality of manual labeling. In this paper, we propose an intelligent framework for automatically recognizing ID information from images of the ID cards. Specifically, we first conduct marginal detection using a multi-operator algorithm and then localize the region of ID card from all the proposed candidate regions with SVM classifier. Furthermore, we segment linguistic characters from the card region by an improved projection algorithm. Finally, we recognize the specific characters by an eight-layer convolutional neural network. We perform extensive experiments on a Chinese ID card dataset to validate the effectiveness and efficiency of our proposed method. The experimental results demonstrate the superiority of proposal over other existing schemes.
Highlights
In recent years, extracting textual information from images has received considerable concerns due to the rapid development of information technology which requires an enormous growth of accessible information
CHARACTER RECOGNITION EXPERIMENTS We present our recognition experiment from the convolution neural network model
According to the above experimental results, our model uses the Rectified Linear Unit (ReLU) activation function, two convolutional layers and one max-pooling layer structure at last, the results show our model is reliable and satisfactory in terms of accuracy requirement of many scenarios
Summary
In recent years, extracting textual information from images has received considerable concerns due to the rapid development of information technology which requires an enormous growth of accessible information. Extracting text information like personal details, iconic numbers printed on smart cards (such as, ID card, and credit card) is a challenge. Those information are commonly used in banks, airports, security company and other places where high accuracy in identification and recording is required. This task can be completed by manual identifying and inputting digital information into system. The rapid development of machine vision techniques enable automatic recognizing information from images with certain
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