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https://github.com/sartaj0/textrecognition-pytorch
Text recognition with Pytorch(CRNN). Provided Onnx model.
https://github.com/sartaj0/textrecognition-pytorch
crnn dnn ocr onnx opencv opencv-dnn pytorch text-recognition
Last synced: about 6 hours ago
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Text recognition with Pytorch(CRNN). Provided Onnx model.
- Host: GitHub
- URL: https://github.com/sartaj0/textrecognition-pytorch
- Owner: sartaj0
- Created: 2021-11-02T12:22:14.000Z (about 3 years ago)
- Default Branch: main
- Last Pushed: 2022-02-01T10:14:32.000Z (almost 3 years ago)
- Last Synced: 2024-08-28T09:46:26.663Z (3 months ago)
- Topics: crnn, dnn, ocr, onnx, opencv, opencv-dnn, pytorch, text-recognition
- Language: Python
- Homepage: https://cv-project-deployed.herokuapp.com/
- Size: 27.3 KB
- Stars: 2
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# TextRecognition-Pytorch
## Onnx Model
From [here](https://drive.google.com/drive/folders/12S3AVgnK-hnjQvKHNyTiTIEIm_5MA7G3?usp=sharing) you can download onnx converted model and use this [file](https://github.com/sartaj0/TextRecognition-Pytorch/blob/main/inferenceWithOnnx.py) for inferencing with opencv.
## Dependency
- pytorch 1.8.1, CUDA 10.2
`pip3 install torch==1.8.1+cu102 torchvision==0.9.1+cu102`
- Requirements: Pillow, opencv-python, tqdm, matplotlib, nltk
`pip3 install Pillow opencv-python tqdm matplotlib nltk`
## Dataset
Download dataset from from [here](https://drive.google.com/drive/folders/1SGNmiD6FvZFS3Qjk5DILIQFTFi_vSx0g?usp=sharing)
data.zip contain below data.
[IIIT5k](http://cvit.iiit.ac.in/projects/SceneTextUnderstanding/IIIT5K.html)[1] [ic17](https://rrc.cvc.uab.es/?ch=8)[2] [ic03](http://www.iapr-tc11.org/mediawiki/index.php/ICDAR_2003_Robust_Reading_Competitions)[3] [IAM Handwriting Database](https://fki.tic.heia-fr.ch/databases/iam-handwriting-database)[4]
## Training and Inference
#### Dataset Format
```
folder
├── data.json
└── data
├── word_1.png
├── word_2.png
├── word_3.png
```
At this time, `data.json` should be `{imagename: label, }`
For example `{word_1: label, ...}`
#### 1. Train
Set The Paramters
```
from train import *
data_path = "E:/dataset/TextRecognition/MixedAll/data"
jsonFilePath = data_path+".json"
model_save_directory = "check_points"
batch_size = 32
rnn_hidden_size = 256
cnn_output_channel = 512
num_epochs = 50
model_backbone = "resnet18"
imgSize = (50, 200)
# imgSize = (32, 100)
imgChannel = 1
lr = 0.000087
train(imgSize, imgChannel, data_path, jsonFilePath, model_backbone, model_save_directory, num_epochs, cnn_output_channel, rnn_hidden_size, batch_size, lr)
```
#### 2. Validation
From training data it will use 20% images for validation
#### 3. Inference
Once the training is completed it will use the best model for onnx conversion
you can use [inferenceWithOnnx.py](https://github.com/sartaj0/TextRecognition-Pytorch/blob/main/inferenceWithOnnx.py) for inferencing with opencv.
output: `develop`
output
***
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***
```
@InProceedings{MishraBMVC12,
author = "Mishra, A. and Alahari, K. and Jawahar, C.~V.",
title = "Scene Text Recognition using Higher Order Language Priors",
booktitle = "BMVC",
year = "2012",
}
```
## References
- [zihaomu](https://github.com/zihaomu/deep-text-recognition-benchmark)
- [gokul karthik](https://www.kaggle.com/gokulkarthik/captcha-text-recognition-using-crnn-in-pytorch)
- [nanonets](https://nanonets.com/blog/deep-learning-ocr/)