An Intelligent Knowledge Extraction Framework for Recognizing Identification Information From Real-World ID Card Images

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.

[1]  Michael S. Lew,et al.  Deep learning for visual understanding: A review , 2016, Neurocomputing.

[2]  Dana H. Ballard,et al.  Generalizing the Hough transform to detect arbitrary shapes , 1981, Pattern Recognit..

[3]  Zhengbing Wang,et al.  Saliency detection integrating both background and foreground information , 2016, Neurocomputing.

[4]  Roberto Manduchi,et al.  Bilateral filtering for gray and color images , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).

[5]  Dong Yu,et al.  Context-Dependent Pre-Trained Deep Neural Networks for Large-Vocabulary Speech Recognition , 2012, IEEE Transactions on Audio, Speech, and Language Processing.

[6]  Yurong Liu,et al.  A survey of deep neural network architectures and their applications , 2017, Neurocomputing.

[7]  Lukasz Kaiser,et al.  Attention is All you Need , 2017, NIPS.

[8]  Ke Lu,et al.  Single image dehazing with a physical model and dark channel prior , 2015, Neurocomputing.

[9]  Yoshua Bengio,et al.  Neural Machine Translation by Jointly Learning to Align and Translate , 2014, ICLR.

[10]  Huchuan Lu,et al.  CNN for saliency detection with low-level feature integration , 2017, Neurocomputing.

[11]  Yann Dauphin,et al.  Language Modeling with Gated Convolutional Networks , 2016, ICML.

[12]  Geoffrey E. Hinton,et al.  Acoustic Modeling Using Deep Belief Networks , 2012, IEEE Transactions on Audio, Speech, and Language Processing.

[13]  Lei Li,et al.  Text Recognition in Mobile Images using Perspective Correction and Text Segmentation , 2016 .

[14]  Guigang Zhang,et al.  Deep Learning , 2016, Int. J. Semantic Comput..

[15]  Jonathan Tompson,et al.  Efficient object localization using Convolutional Networks , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[16]  Ming-Wei Chang,et al.  BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding , 2019, NAACL.

[17]  Lei Guo,et al.  Object Detection in Optical Remote Sensing Images Based on Weakly Supervised Learning and High-Level Feature Learning , 2015, IEEE Transactions on Geoscience and Remote Sensing.

[18]  Lei Yang,et al.  An improved Sobel edge detection , 2010, 2010 3rd International Conference on Computer Science and Information Technology.

[19]  Zhiyong Gao,et al.  Edge guided salient object detection , 2017, Neurocomputing.

[20]  Jason Weston,et al.  A unified architecture for natural language processing: deep neural networks with multitask learning , 2008, ICML '08.

[21]  Liangpei Zhang,et al.  Sparse Transfer Manifold Embedding for Hyperspectral Target Detection , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[22]  Geoffrey E. Hinton,et al.  Rectified Linear Units Improve Restricted Boltzmann Machines , 2010, ICML.

[23]  Yu Li,et al.  Automatic Target Detection in High-Resolution Remote Sensing Images Using Spatial Sparse Coding Bag-of-Words Model , 2012, IEEE Geoscience and Remote Sensing Letters.

[24]  Zhang Yi,et al.  Improving local minima of columnar competitive model for TSPs , 2006, IEEE Transactions on Circuits and Systems I: Regular Papers.

[25]  Yoshua Bengio,et al.  Gradient-based learning applied to document recognition , 1998, Proc. IEEE.

[26]  Jürgen Schmidhuber,et al.  Multi-column deep neural networks for image classification , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[27]  M. Student,et al.  Sobel Edge Detection Algorithm , 2013 .

[28]  Zhang Yi,et al.  Real-Time Robot Path Planning Based on a Modified Pulse-Coupled Neural Network Model , 2009, IEEE Transactions on Neural Networks.

[29]  Gangyi Jiang,et al.  Unsupervised segmentation of natural images based on statistical modeling , 2017, Neurocomputing.

[30]  Honglak Lee,et al.  Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations , 2009, ICML '09.

[31]  Chao Yao,et al.  Approximative Bayes optimality linear discriminant analysis for Chinese handwriting character recognition , 2016, Neurocomputing.

[32]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[33]  Baoming Shan.  Vehicle License Plate Recognition Based on Text-line Construction and Multilevel RBF Neural Network , 2011, J. Comput..

[34]  Junwei Han,et al.  Object detection in remote sensing imagery using a discriminatively trained mixture model , 2013 .

[35]  Junwei Han,et al.  Learning Rotation-Invariant Convolutional Neural Networks for Object Detection in VHR Optical Remote Sensing Images , 2016, IEEE Transactions on Geoscience and Remote Sensing.

[36]  Lu Yang,et al.  Recognition of the smart card iconic numbers , 2016 .

[37]  Lawrence D. Jackel,et al.  Handwritten Digit Recognition with a Back-Propagation Network , 1989, NIPS.

[38]  Xiang Zhang,et al.  OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks , 2013, ICLR.

[39]  Ali Farhadi,et al.  You Only Look Once: Unified, Real-Time Object Detection , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[40]  John F. Canny,et al.  A Computational Approach to Edge Detection , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.