Optimizing Whiteboard Digitization with Clarix: An Automated Vision-Based System for Real-Time Text Extraction and Surface Cleaning

Whiteboard digitization in educational settings grapples with challenges posed by handwritten text, mathematical notation, and unstructured layouts that confound traditional Optical Character Recognition (OCR) systems. Clarix, a low-cost embedded system, addresses these issues by integrating a Raspberry Pi, high-resolution camera, and servo-driven erasure mechanism to automate content capture, text extraction, and archiving as searchable PDFs. This study benchmarks five multi-modal Large Language Models (LLMs) (GPT-4o, Claude, Gemini, DeepSeek, Grok) against traditional OCR systems (PaddleOCR, EasyOCR, TesseractOCR) for extracting whiteboard content across mathematics, physics, and economics domains. Results highlight a stark performance divide: multimodal LLMs achieved F1-scores ranging from 0.7550 to 0.8466, with Gemini leading at 0.8466 (precision 0.8188, recall 0.8784), followed by GPT- 4o (F1=0.8162) and DeepSeek (F1=0.7965), while PaddleOCR topped traditional systems with an F1-score of 0.3333 (precision 0.3523, recall 0.3223), followed by EasyOCR (0.1158) and Tesser-actOCR (0.0000). Notably, increased region detection correlated with diminished performance, underscoring the superiority of contextual understanding over exhaustive segmentation. Clarix’s fusion of intelligent automation and advanced text processing marks a transformative advancement in bridging analog and digital educational environments.