Proceedings ArticleDOI
Fine-grained classification of identity document types with only one example
Marcel Simon,Erik Rodner,Joachim Denzler +2 more
- pp 126-129
TLDR
Different techniques for recognizing types of partly very similar identity documents using state-of-the-art visual recognition approaches including feature representations based on recent achievements with convolutional neural networks are developed and evaluated.Abstract:
In this paper, we tackle the task of recognizing types of partly very similar identity documents using state-of-the-art visual recognition approaches. Given a scanned document, the goal is to identify the country of issue, the type of document, and its version. Whereas recognizing the individual parts of a document with known standardized layout can be done reliably, identifying the type of a document and therefore also its layout is a challenging problem due to the large variety of documents. In our paper, we develop and evaluate different techniques for this application including feature representations based on recent achievements with convolutional neural networks. On a dataset with 74 different classes and using only one training image per class, our best approach achieves a mean class-wise accuracy of 97.7%.read more
Citations
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Journal ArticleDOI
MIDV-500: A Dataset for Identity Documents Analysis and Recognition on Mobile Devices in Video Stream
TL;DR: This paper presents a Mobile Identity Document Video dataset (MIDV-500) consisting of 500 video clips for 50 different identity document types with ground truth which allows to perform research in a wide scope of document analysis problems.
Proceedings ArticleDOI
Analysis of Convolutional Neural Networks for Document Image Classification
Chris Tensmeyer,Tony Martinez +1 more
TL;DR: In this article, the authors conducted a large empirical study to find what aspects of CNNs most affect performance on document images and showed that CNNs trained on RVL-CDIP learn region-specific layout features.
Proceedings ArticleDOI
Complex Document Classification and Localization Application on Identity Document Images
TL;DR: This paper addresses the classification of documents composed of few textual information and complex background (such as identity documents) and shows that training images are not necessary and only one reference image is enough to create a document model.
Proceedings ArticleDOI
Fast Method of ID Documents Location and Type Identification for Mobile and Server Application
TL;DR: Results show that using lines and quadrangles increase the location accuracy, and the proposed algorithm surpasses previously published works in classification precision and computational performance.
Book ChapterDOI
Machine Learning Techniques for Identity Document Verification in Uncontrolled Environments: A Case Study
Alejandra Castelblanco,Jesus Solano,Christian Lopez,Esteban Rivera,Lizzy Tengana,Martín Ochoa +5 more
TL;DR: A machine-learning based pipeline to process pictures of documents in distributed enrollment to services such as banking that relies on various analysis modules and visual features for verification of document type and legitimacy is presented.
References
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Posted Content
Caffe: Convolutional Architecture for Fast Feature Embedding
Yangqing Jia,Evan Shelhamer,Jeff Donahue,Sergey Karayev,Jonathan Long,Ross Girshick,Sergio Guadarrama,Trevor Darrell +7 more
TL;DR: Caffe as discussed by the authors is a BSD-licensed C++ library with Python and MATLAB bindings for training and deploying general-purpose convolutional neural networks and other deep models efficiently on commodity architectures.
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Object Detection with Discriminatively Trained Part-Based Models
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DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition
TL;DR: DeCAF, an open-source implementation of deep convolutional activation features, along with all associated network parameters, are released to enable vision researchers to be able to conduct experimentation with deep representations across a range of visual concept learning paradigms.