Robotic OCR-Based Pallet Label Verification: Experimental Evaluation and Inventory Database Validation

Background: Pallet-label identification underpins warehouse inventory accuracy, but how imaging geometry affects optical character recognition (OCR) of robot-captured labels has not been characterized. This study evaluates a robotic OCR pipeline with database validation. Methods: An Ava Telepresence Robot captured label images, which the Google Cloud Vision API read and a simulated warehouse management system validated. A full-factorial experiment varied font size (12–16 pt), distance (1.00–1.50 m), and horizontal angle (0–60°) across 108 images; 120 validation reads assessed identifier accuracy. Results: In a conservative analysis, all three main effects on OCR confidence were significant (p ≤ 0.006). Confidence was stable to 45° at 1.00 m and, for 16 pt, at all distances, but fell for smaller fonts at longer distances and, in most conditions, at 60°. Cloud Vision read 80.8% of serial numbers exactly (character error rate 1.9%), against 37.5% for Tesseract. A calibrated 88.5% confidence criterion defined an operating envelope of 16 pt to 1.50 m and 14 pt to 1.25 m at angles up to 45°. Database validation produced no false accepts. Conclusions: Font size, distance, and viewing angle bound reliable label verification; the envelope guides label printing and camera placement, pending confirmation with repositioned replicates.