OCR, Object Detection, Number Plate, Real Time

Kaven Lee

Last update: Dec 6, 2022

Related tags

Computer Vision wpod_ocr

Overview

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

https://github.com/sameerbairwa/ANPR

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

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OCR, Object Detection, Number Plate, Real Time

Related tags

Overview

README.md

PrePareded

OCR Recognition Training

Detect Number Plate

Excution

Performance

Reference

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Releases(wpod)

wpod(Feb 4, 2022)

Owner

Kaven Lee

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