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.

A TextContextAware CNN Network for Multioriented and Multilanguage Scene Text Detection The existing deep learning based stateoftheart scene text detection methods treat scene texts a type of general objects, or segment text regions directly. The latter category achieves remarkable detection results on arbitraryorientation and large aspect ratios of scene texts based on instance segmentation algorithms. However, due to the lack of context information with consideration of scene text unique characteristics, directly applying instance segmentation to text detection task is prone to result in low accuracy, especially producing false positive detection results. To ease this problem, we propose a novel textcontextaware scene text detection CNN structure, which appropriately encodes channel and spatial attention information to construct contextaware and discriminative feature map for multioriented and multilanguage text detection tasks. With high representation ability of textcontextaware feature map, the proposed instance segmentation based method can not only robustly detect multioriented and multilanguage text from natural scene images, but also produce better text detection results by greatly reducing false positives. Experiments on ICDAR2015 and ICDAR2017MLT datasets show that the proposed method has achieved superior performances in precision, recall and Fmeasure than most of the existing studies.