AI-Driven Probabilistic Region-of-Interest Guided Optical Character Recognition (OCR) for Autonomous Traffic Sign Detection: A Python-Based Deep Learning Framework with Elastic Weight Consolidation

Accurate and real-time road sign recognition is a safety-critical capability in autonomous driving systems, where detection latency directly impacts vehicle response and passenger safety. This paper presents an AI-driven, fully Python-based Optical Character Recognition (OCR) pipeline for autonomous traffic sign detection, incorporating a probabilistic Region of Interest (ROI) scanning strategy guided by historical spatial detection maps and a Long Short-Term Memory (LSTM) temporal predictor. Rather than processing full camera frames at high computational cost, the proposed system intelligently narrows its scanning focus to frame zones with the highest statistical likelihood of containing traffic signs, reducing the effective pixel evaluation area by up to 78% per frame. The pipeline achieves a mean Average Precision (mAP) of 97.8% at an inference latency of 31 ms on CPU and 10.3 ms on GPU — a 78% latency reduction and 6.6 pp accuracy improvement over the full-frame scanning baseline. A continuous AI self-improvement module employing Elastic Weight Consolidation (EWC) and memory replay enables incremental retraining from 94.2% to 98.4% validation accuracy across six rounds with negligible catastrophic forgetting.