Automated data extraction of bar chart raster images
This addresses the need for efficient data extraction from scientific charts in fields like clinical trials, though it is incremental as it builds on existing OCR methods.
The researchers tackled the problem of automatically extracting data from bar chart images for meta-analysis by developing software using optical character recognition, achieving 91.8% agreement with manual extraction and accuracies up to 88.6% for specific chart elements.
Objective: To develop software utilizing optical character recognition toward the automatic extraction of data from bar charts for meta-analysis. Methods: We utilized a multistep data extraction approach that included figure extraction, text detection, and image disassembly. PubMed Central papers that were processed in this manner included clinical trials regarding macular degeneration, a disease causing blindness with a heavy disease burden and many clinical trials. Bar chart characteristics were extracted in both an automated and manual fashion. These two approaches were then compared for accuracy. These characteristics were then compared using a Bland-Altman analysis. Results: Based on Bland-Altman analysis, 91.8% of data points were within the limits of agreement. By comparing our automated data extraction with manual data extraction, automated data extraction yielded the following accuracies: X-axis labels 79.5%, Y-tick values 88.6%, Y-axis label 88.6%, Bar value <5% error 88.0%. Discussion: Based on our analysis, we achieved an agreement between automated data extraction and manual data extraction. A major source of error was the incorrect delineation of 7s as 2s by optical character recognition library. We also would benefit from adding redundancy checks in the form of a deep neural network to boost our bar detection accuracy. Further refinements to this method are justified to extract tabulated and line graph data to facilitate automated data gathering for meta-analysis.