README.md

PrePareded

• anaconda env
• requirements.txt
• clova AI → deep text recognition → trained weights (ex, .pth)
• wpod-net weights (ex, .h5 , .json)
• https://github.com/KavenLee/wpod_ocr/releases/tag/wpod
• Make Dir ‘weights’ and put the downloaded file in the Dir

OCR Recognition Training

• train data, validation data (ex, image)
• train & val labelling data (ex, .txt)
• train & val data split using ocr_label.py & train_test_split.py
• Label (ex, {imagepath} \t {label} \n ) → a.jpg(\t)apple(\n)

• cd d:\Library\deep-text-recognition-benchmark
• using train.py, create-lmdb.py

Detect Number Plate

• using wpod-net
• get high accuracy if only a car existed in image file.
• using openCV image
• using PIL Image on OCR

Excution

• using a image
• success

• Failed

• using video

Performance

• No GPU, Only CPU (OS : Windows)
  • Detect Number Plate time → Iamge : 100 ~ 180ms ,Video : 200ms
  • OCR Recognition time → Image : 50ms , Video : 80 ~ 100 ms
  • Total time → 200~300ms
• Using GPU on Jetson Tx2 (OS : Linux)
  • Detect Number Plate time → Image : 3 ~ 400ms
  • OCR Recognition time → Image : 100~ 200ms
  • Total time → 400~600 ms

Reference

https://github.com/clovaai/deep-text-recognition-benchmark

네이버 deep-text-recognition 모델을 custom data로 학습 & 아키텍쳐 분석

GitHub

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