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http://hdl.handle.net/10609/73186
Title: | Classification of identity documents using a deep convolutional neural network |
Author: | Vilàs Mari, Pere |
Tutor: | Meler Corretjé, Lourdes |
Others: | Universitat Oberta de Catalunya García-Solórzano, David Morán Moreno, Jose Antonio |
Abstract: | In this work, we review the main subjects that we have to serve to build an image classifier, then we design and implement a system to classify identity documents. Especially relevant is the description of the workflow that we have come up with after several ways of approaching the problem and which we hope can serve other machine learning practitioners. We evaluate some fea- ture extractor algorithms to find the most suitable for identity documents classifi- cation purposes. Using virtual machines on the cloud, we run feature extractors in parallel to label at a speed of 16 images/s. Then, we select a neural network architecture and hyperparameters to train a convolutional neural network. Our results give an accuracy of 98%. We detail the responses of the convolutional filters over the images. Results and source code in the appendix. |
Keywords: | machine learning computer vision identity document |
Document type: | info:eu-repo/semantics/bachelorThesis |
Issue Date: | 20-Jan-2018 |
Publication license: | http://creativecommons.org/licenses/by-nc-nd/3.0/es/ |
Appears in Collections: | Bachelor thesis, research projects, etc. |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
Presentacio-identity-doc-classificator.mp4 | Video de presentació del treball | 154,24 MB | MP4 | View/Open |
pvilasTFM0118memoria.pdf | Memoria del TFM | 7,89 MB | Adobe PDF | View/Open |
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