Automatic Recognition of Learning Resource Category in a Digital Library (2401.12220v1)
Abstract: 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.
- N. Chen and D. Blostein, “A survey of document image classification: problem statement, classifier architecture and performance evaluation,” International Journal of Document Analysis and Recognition (IJDAR), vol. 10, no. 1, pp. 1–16, 2007.
- L. Kang, J. Kumar, P. Ye, Y. Li, and D. Doermann, “Convolutional neural networks for document image classification,” in 2014 22nd International Conference on Pattern Recognition. IEEE, 2014, pp. 3168–3172.
- A. W. Harley, A. Ufkes, and K. G. Derpanis, “Evaluation of deep convolutional nets for document image classification and retrieval,” in 2015 13th International Conference on Document Analysis and Recognition (ICDAR). IEEE, 2015, pp. 991–995.