Automated Line Chart Information Extraction for Accelerated Data Analysis in Chemical Science using Neural Networks and Hough Transform

Researchers want to automate the extraction of information from charts in paper, that will help do fast reading and compare methods or results efficiency, especially in chemical science research progress. This paper designs and implements a line chart information extraction algorithm using neural networks and other techniques. First, a large dataset of line charts was collected and annotated to provide a foundation for neural network training. Then, the Hough transform line detection algorithm was used to detect the axes, and the line charts were segmented. For each segmented part, different recognition algorithms were designed to identify various elements in the line charts, including axes, line regions, and legends. To validate the effectiveness of the algorithm, experimental tests were conducted in the field of inorganic catalysis, automatically extracting information from line charts in the literature. The experimental results show that the designed algorithm can accurately recognize various elements in line charts and effectively extract experimental data. Compared with traditional manual methods, automated extraction not only saves a considerable amount of time but also improves the accuracy and consistency of data extraction on paper fast reading. In summary, this method provides researchers with an efficient tool that accelerates the acquisition and comparison of experimental data, thereby advancing the progress of related research electronic document.