OCR, Object Detection, Number Plate, Real Time

Related tags

Computer Visionwpod_ocr
Overview

README.md

PrePareded

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)

Untitled

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

Untitled

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

bus3-suc.png

bus4-suc.png

1.png

2.png

  • Failed

m1.png

m2.png

bus1.png

bus2.png

  • using video

video-1.png

video-2.png

video-3.png

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

https://github.com/sameerbairwa/ANPR

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

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