Automatic Recognition of Learning Resource Category in a Digital Library
This addresses the challenge of efficient metadata extraction for digital library administrators, but it is incremental as it applies existing methods to a new dataset.
The paper tackled the problem of automatically categorizing diverse document types in digital libraries to reduce manual metadata tagging, by introducing the Heterogeneous Learning Resources dataset and using OCR with state-of-the-art classifiers to predict document labels from constituent images.
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.