Towards an efficient framework for Data Extraction from Chart Images
This work addresses the challenge of automating data extraction from infographics for data mining applications, representing an incremental improvement over existing methods.
The paper tackles the problem of extracting data from chart images by developing a system that uses deep learning for plot element detection and data conversion, achieving high precision in detecting boxes and distinguishing close points without heuristic assumptions.
In this paper, we fill the research gap by adopting state-of-the-art computer vision techniques for the data extraction stage in a data mining system. As shown in Fig.1, this stage contains two subtasks, namely, plot element detection and data conversion. For building a robust box detector, we comprehensively compare different deep learning-based methods and find a suitable method to detect box with high precision. For building a robust point detector, a fully convolutional network with feature fusion module is adopted, which can distinguish close points compared to traditional methods. The proposed system can effectively handle various chart data without making heuristic assumptions. For data conversion, we translate the detected element into data with semantic value. A network is proposed to measure feature similarities between legends and detected elements in the legend matching phase. Furthermore, we provide a baseline on the competition of Harvesting raw tables from Infographics. Some key factors have been found to improve the performance of each stage. Experimental results demonstrate the effectiveness of the proposed system.