Integrating Coordinates with Context for Information Extraction in Document Images Information extraction from document collections is a fundamental and important step to understand, structure and analyze data. Many approaches with rules and deep learning based techniques have been applied on plain text, however, when it comes to document images, such demand still exists but becomes quite challenging without linguistic knowledge. In this paper, we propose an approach to extract required named entities (NEs) from document images by integrating the coordinate information from the detection and recognition stage into the contextual information of the BiLSTMCRF model with an attention mechanism. We test this method on two realworld datasets. One is a Contract Dataset of Listed Companies, and the other is an Insurance Policy Dataset of our own. The result shows a combination of coordinates and context with attention leverages extraction in document images, opening up potential applications on such tasks.

A Neural Approach for Text Extraction from Scholarly Figures In recent years, the problem of scene text extraction from images has received extensive attention and significant progress. However, text extraction from scholarly figures such as plots and charts remains an open problem, in part due to the difficulty of locating irregularly placed text lines. To the best of our knowledge, literature has not described the implementation of a text extraction system for scholarly figures that adapts deep convolutional neural networks used for scene text detection. In this paper, we propose a text extraction approach for scholarly figures that forgoes preprocessing in favor of using a deep convolutional neural network for text line localization. Our system uses a publicly available scene text detection approach whose network architecture is well suited to text extraction from scholarly figures. Training data are derived from charts in arXiv papers which are extracted using Allen Instituteu0027s pdffigures tool. Since this tool analyzes PDF data as a container format in order to extract text location through the mechanisms which render it, we were able to gather a large set of labeled training samples. We show significant improvement from methods in the literature, and discuss the structural changes of the text extraction pipeline.