Complex Document Classification and Localization Application on Identity Document Images
- 1 November 2017
- conference paper
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- Vol. 01, 426-431
- https://doi.org/10.1109/icdar.2017.77
Abstract
This paper studies the problem of document image classification. More specifically, we address the classification of documents composed of few textual information and complex background (such as identity documents). Unlike most existing systems, the proposed approach simultaneously locates the document and recognizes its class. The latter is defined by the document nature (passport, ID, etc.), emission country, version, and the visible side (main or back). This task is very challenging due to unconstrained capturing conditions, sparse textual information, and varying components that are irrelevant to the classification, e.g. photo, names, address, etc. First, a base of document models is created from reference images. We show that training images are not necessary and only one reference image is enough to create a document model. Then, the query image is matched against all models in the base. Unknown documents are rejected using an estimated quality based on the extracted document. The matching process is optimized to guarantee an execution time independent from the number of document models. Once the document model is found, a more accurate matching is performed to locate the document and facilitate information extraction. Our system is evaluated on several datasets with up to 3042 real documents (representing 64 classes) achieving an accuracy of 96.6%.Keywords
This publication has 22 references indexed in Scilit:
- Discriminative part model for visual recognitionComputer Vision and Image Understanding, 2015
- Fine-grained classification of identity document types with only one examplePublished by Institute of Electrical and Electronics Engineers (IEEE) ,2015
- Document classification with distributions of word vectorsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2014
- Geometry-constrained spatial pyramid adaptation for image classificationPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2014
- Multimodal page classification in administrative document image streamsInternational Journal on Document Analysis and Recognition (IJDAR), 2014
- Learning and Transferring Mid-level Image Representations Using Convolutional Neural NetworksPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2014
- Unsupervised Classification of Structurally Similar Document ImagesPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2013
- SURF: Speeded Up Robust FeaturesLecture Notes in Computer Science, 2006
- Hidden tree markov models for document image classificationIeee Transactions On Pattern Analysis and Machine Intelligence, 2003
- Fine-grained document genre classification using first order random graphsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002