Automatic Recognition of Learning Resource Category in a Digital Library

S. Banerjee, Debarshi Kumar Sanyal, S. Chattopadhyay, Plaban Kumar Bhowmick, P. Das
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Abstract

Digital libraries generally need to process a large volume of diverse document types. The collection and tagging of metadata is a long, error-prone, workforce-consuming task. We are attempting to build an automatic metadata extractor for digital libraries. In this work, we present the Heterogeneous Learning Resources (HLR) dataset for document image classification. The individual learning resource is first decomposed into its constituent document images (sheets) which are then passed through an OCR tool to obtain the textual representation. The document image and its textual content are classified with state-of-the-art classifiers. Finally, the labels of the constituent document images are used to predict the label of the overall document.
数字图书馆中学习资源类别的自动识别
数字图书馆通常需要处理大量不同类型的文档。元数据的收集和标记是一项耗时、容易出错、耗费人力的任务。我们正在尝试为数字图书馆构建一个自动元数据提取器。在这项工作中,我们提出了用于文档图像分类的异构学习资源(HLR)数据集。单个学习资源首先被分解成其组成的文档图像(表),然后通过OCR工具获得文本表示。使用最先进的分类器对文档图像及其文本内容进行分类。最后,利用组成文档图像的标签来预测整个文档的标签。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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