Deep Learning for the Detection of Tabular Information from Electronic Component Datasheets The global electronic components supply chain consists of tens of thousands of ecomponent 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 uptodate on this information consumes a great deal of human effort and corporate resources. Based on the motivation that AIbased 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 learningbased object detectors are shown to be state of the art in detection tasks in different domains therefore we chose two cuttingedge models to adapt to this field, namely FasterRCNN and RetinaNet. We use backbone networks which are pretrained 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 FasterRCNN 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.

Deep Learning for Recognizing the Anatomy of Tables on Datasheets With the growth of information flow through supply chains, we address the issue of semantically segmenting tabular data from document flows. This challenge is abstract in definition as there is no guideline to what defines a table, therefore we primarily applied deep learning methods to compare performances, as well as a morphologybased method to compare several methods for the task of table structure detection. Taking advantage of transfer learning, we found that MaskRCNN was the most capable network at abstracting to segmentation of tables. Due to the large variation in sizes between columns and rows, most networks failed to detect both with equal effectiveness. However with MaskRCNN 97.01% precision and 98.28% recall were attained, which put it far ahead of other models in row detection, cementing MaskRCNN as the most effective choice in this task.