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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2401.12220 |
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| _version_ | 1866911762880659456 |
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| author | Banerjee, Soumya Sanyal, Debarshi Kumar Chattopadhyay, Samiran Bhowmick, Plaban Kumar Das, Partha Pratim |
| author_facet | Banerjee, Soumya Sanyal, Debarshi Kumar Chattopadhyay, Samiran Bhowmick, Plaban Kumar Das, Partha Pratim |
| contents | Digital libraries often face the challenge of processing a large volume of diverse document types. The manual collection and tagging of metadata can be a time-consuming and error-prone task. To address this, we aim to develop an automatic metadata extractor for digital libraries. In this work, we introduce the Heterogeneous Learning Resources (HLR) dataset designed for document image classification. The approach involves decomposing individual learning resources into constituent document images (sheets). These images are then processed through an OCR tool to extract textual representation. State-of-the-art classifiers are employed to classify both the document image and its textual content. Subsequently, the labels of the constituent document images are utilized to predict the label of the overall document. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_12220 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Automatic Recognition of Learning Resource Category in a Digital Library Banerjee, Soumya Sanyal, Debarshi Kumar Chattopadhyay, Samiran Bhowmick, Plaban Kumar Das, Partha Pratim Digital Libraries Computer Vision and Pattern Recognition Digital libraries often face the challenge of processing a large volume of diverse document types. The manual collection and tagging of metadata can be a time-consuming and error-prone task. To address this, we aim to develop an automatic metadata extractor for digital libraries. In this work, we introduce the Heterogeneous Learning Resources (HLR) dataset designed for document image classification. The approach involves decomposing individual learning resources into constituent document images (sheets). These images are then processed through an OCR tool to extract textual representation. State-of-the-art classifiers are employed to classify both the document image and its textual content. Subsequently, the labels of the constituent document images are utilized to predict the label of the overall document. |
| title | Automatic Recognition of Learning Resource Category in a Digital Library |
| topic | Digital Libraries Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2401.12220 |