Deep Learning for the Detection of Tabular Information from Electronic Component Datasheets

The global electronic components supply chain consists of tens of thousands of e-component manufacturers who fabricate over a billion distinct components. These are described in datasheets that differ in style, layout and content, and frequently publish the salient product information in tables. Keeping up-to-date on this information consumes a great deal of human effort and corporate resources. Based on the motivation that AI-based techniques are strong candidates to minimize human intervention in many applications, in this paper, we aim at the first stage of this problem and conduct a comparison of deep learning methods in detecting tabular elements in these documents. Deep learning-based object detectors are shown to be state of the art in detection tasks in different domains therefore we chose two cutting-edge models to adapt to this field, namely Faster-RCNN and RetinaNet. We use backbone networks which are pre-trained on visually salient datasets then employ transfer learning techniques to adapt to our domain. We compare the two networks under two different datasets, namely a dataset that is widely used in academic studies and a private dataset that is used by the suppliers in real supply chains. Our numerical results show that the two networks adapt well to the domain with Faster-RCNN exhibiting marginally better precision with more than 1% difference. However, RetinaNet stands out with promising recall values indicating Feature Pyramid Network architecture can potentially detect technical documents better.