Problem
Research questions and friction points this paper is trying to address.
axis calibration
curve extraction
line plots
LinePilot Digitizer: Line-Plot Recovery with Manual and Automatic Calibration
🤖 AI Summary
该研究解决了从线图中恢复数值序列的问题，通过结合基于颜色的连续曲线恢复与三种校准模式，并引入了DigitizerBench基准来评估性能。
📝 Abstract
Recovering numerical series from line plots requires accurate axis calibration and reliable curve extraction. We present LinePilot Digitizer (LinePilot), which combines continuous color-based curve recovery with three calibration modes: LinePilot (standard), LinePilot (enhanced), and LinePilot (OCR). We also introduce DigitizerBench, the first dedicated benchmark for systematically evaluating digitizer performance, using an orthogonal design spanning signal, rendering, and plot-structure factors with complementary automatic and human-guided evaluations. We evaluate performance using failure-penalized capped normalized root-mean-square error (FPC-NRMSE), which assigns unit loss to missing, unusable, or catastrophically inaccurate outputs. On DigitizerBench-Full, LinePilot (OCR) achieves the lowest mean FPC-NRMSE (0.672) and highest trusted usability (38.2%) among the tested automatic pipelines. On DigitizerBench-Lite, LinePilot (enhanced) achieves the lowest mean FPC-NRMSE (0.081), 100% output success, and highest trusted usability (93.3%). The orthogonal benchmark design further enables factor analysis to identify the factors that most significantly affect digitizer performance. Together, the three calibration modes provide a practical trade-off between automation, user control, and accuracy within a shared curve-recovery workflow.
Innovation
Methods, ideas, or system contributions that make the work stand out.
LinePilot
curve recovery
calibration modes
DigitizerBench
FPC-NRMSE
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