A computer vision-based approach for automatically extracting data from bar chart raster images to facilitate...

Although bar charts are widely used in scientific publications, their rasterized format within Portable Document Format (PDF) files complicates automated data extraction, hindering large-scale evidence synthesis and meta-analysis. To address this, we developed and evaluated an automated pipeline for extracting quantitative data from bar charts embedded in the biomedical literature. The four-stage pipeline comprises (1) image extraction and panel segmentation, (2) optical character recognition (OCR)-based text detection, (3) image disassembly to identify chart components, and (4) data reconstruction using numeric parsing and axis-based interpolation. The system combines edge detection, morphological operations, and convolutional neural network (CNN)-based figure classification using a transfer-learned Inception v3 model. Performance was validated on randomized controlled trials in age-related macular degeneration, with manually annotated values from a semi-automated labeling tool as the reference standard, and agreement was assessed using Bland-Altman analysis. Across 28 bar charts from ten publications, the pipeline correctly recognized 92.9% (95% confidence interval [CI], 77.4-98.0) of figure types, 96.0% (95% CI, 94.2-97.3) of text blocks, and 79.1% (95% CI, 74.7-83.0) of bars. For numerical reconstruction, 81.2% (95% CI, 76.3-85.2) of bar values fell within ±5% of the reference standard, 63.0% (95% CI, 57.3-68.3) within ±2%, and 48.6% (95% CI, 43.0-54.3) within ±1%. Bland-Altman analysis showed a small negative bias of -0.18 (95% CI, -0.34 to -0.02), with 94.9% of differences within the limits of agreement. Most outliers arose from OCR digit misclassification or ambiguous bar boundaries. This proof-of-concept study demonstrates the feasibility of automated data extraction from bar charts using a hybrid approach that combines image-processing heuristics with CNN-based classification. Although currently limited to bar charts and a single clinical domain, the pipeline represents a step toward scalable, end-to-end systems for automated evidence extraction to support meta-analyses across the biomedical literature.