A Multimodal Approach to Autonomous Document Categorization Using Convolutional Neural Networks
When international students apply for the Swedish educational system, they send documents to verify their merits. These documents are categorized and evaluated by administrators. This thesis approach the problem of document classification with a multimodal convolutional network. By looking at both i...
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| Format: | Others |
| Language: | English |
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Umeå universitet, Institutionen för datavetenskap
2018
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| Online Access: | http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-156289 |
| Summary: | When international students apply for the Swedish educational system, they send documents to verify their merits. These documents are categorized and evaluated by administrators. This thesis approach the problem of document classification with a multimodal convolutional network. By looking at both image and text features together, it is examined if the classification is better than any of the sources alone. The best result for single source classification was when the input was text at 85.2% accuracy, this was topped by the multimodal approach with a accuracy of 88.4%.This thesis concludes that there is a gain in accuracy when using a multimodal approach. |
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