PGNet baseline finalized at step1-20260520_0937 (epoch 8, f_score_e2e 0.144).
Documented diagnostic history including misdiagnoses (cv2 viz misled "Korean
failure", char-level polygon hypothesis disproved by code reading) and
verified facts (single-line Korean works, two-line region names fail,
official PGNet step1 is "still low" by design, no Korean PGNet pretrained
publicly exists).
Next direction shifted toward YOLO + PaddleOCR 2-stage given (a) no Korean LP
PGNet validation cases in literature, (b) standard Korean LP pipeline uses
2-stage with reported 95%+ accuracy, (c) PGNet step2 effect unverifiable
without real data.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
PaddleOCR PGNet 기반 한국 번호판(LP) end-to-end 검출 + OCR 학습 프로젝트.
대상 번호판: 승용(흰), 영업용(노란), 전기차(파란 8자리), 화물·특수.
디렉토리 구조
kr_lp_pgnet/
├── configs/ # PGNet 학습 config (.yml)
├── dict/ # 문자 사전 (kr_lp_dict.txt)
├── data_gen/ # 합성 LP 이미지 생성기
├── scripts/ # 서버 셋업·학습 실행 셸 스크립트
└── tools/ # 라벨 검증·시각화 등 보조 스크립트
서버 측 실행 순서
# 1. 최초 1회: 환경 셋업
docker exec kr_lp_pgnet bash scripts/setup_server.sh
# 2. Step1: 합성 데이터 생성 + 학습
docker exec -d kr_lp_pgnet bash scripts/run_step1.sh