"AIDLC team on AMD AI League Hackathon"

ROADREAD is a high-precision, low-latency road sign and license plate recognition system engineered specifically for AMD ROCm hardware (AMD Radeon Pro W7900D, 48 GiB VRAM, RDNA3 gfx1100). Built for the AMD AI Developer Program Mini-Challenge 2, the pipeline accurately transcribes unseen and severely degraded imagery across six challenging domains: US license plates, Chinese license plates, word-based traffic signs, worded speed limits, advisory plaques, and multiline warning signs. To overcome the cold-start overhead of heavy machine learning frameworks on ROCm and strictly honor the 30-second per-image evaluation deadline, ROADREAD implements a decoupled client-worker architecture. A zero-dependency, standard-library Python client handles evaluator requests in under 10 ms, communicating over a local IPC socket with a pre-warmed resident worker daemon. The vision-language model (Qwen-VL in BF16 precision) remains resident in GPU memory, eliminating redundant weight loading and memory allocations across sequential requests. The transcription pipeline uses an adaptive escalation strategy: standard inputs are processed at a bounded 1.0 MP resolution, while ambiguous or severely degraded samples trigger a single high-resolution escalation pass under internal budget controls. Post-processing strictly enforces domain-specific formatting rules, including US banner and slogan scrubbing, full-width ASCII normalization, serial-only I/O digit correction for Chinese plates, and character-repetition safety without heuristic truncation. Validated under the official Docker container baseline (rocm/pytorch:rocm10.0_ubuntu26.04_py3.14_pytorch_release_2.13.0) within the 60 GiB uncompressed boundary, ROADREAD achieves 100% exact-match accuracy on published samples with a median latency of 0.565 seconds, and 90.8% exact-match accuracy on synthetic stress-test benchmarks with zero timeout violations.