Ratio of true positives to all actual positives, measuring how many positives are found. This metric is fundamental for evaluating system performance in OCR and document understanding tasks.
Importance in OCR
Accurate measurement of recall enables researchers and practitioners to quantify improvements, compare different approaches, and ensure quality control in production systems. Modern OCR systems like DeepSeek-OCR optimize for this metric during training and validation.
Calculation and Interpretation
The metric is calculated using standardized formulas that compare predicted outputs against ground truth annotations. Higher values generally indicate better performance, though the interpretation depends on the specific application context and requirements.
Practical Applications
In document processing pipelines, monitoring recall helps identify problematic documents, track system performance over time, and trigger alerts when quality falls below acceptable thresholds. Enterprise applications often set minimum thresholds for automated processing.
Optimization Strategies
Improving recall involves multiple approaches including better preprocessing, advanced neural network architectures, ensemble methods, and post-processing corrections. The choice of optimization strategy depends on computational budget, latency requirements, and target accuracy levels.