Metadata-Version: 2.1
Name: ocr_tamil
Version: 0.0.8
Summary: Python Tamil OCR package
Home-page: https://github.com/gnana70/tamil_ocr
Author: Gnana Prasath
Author-email: Gnana Prasath <gnana70@gmail.com>
License: MIT License
        
        Copyright (c) 2024 Gnana Prasath
        
        Permission is hereby granted, free of charge, to any person obtaining a copy
        of this software and associated documentation files (the "Software"), to deal
        in the Software without restriction, including without limitation the rights
        to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
        copies of the Software, and to permit persons to whom the Software is
        furnished to do so, subject to the following conditions:
        
        The above copyright notice and this permission notice shall be included in all
        copies or substantial portions of the Software.
        
        THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
        IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
        FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
        AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
        LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
        OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
        SOFTWARE.
        
Project-URL: Homepage, https://github.com/gnana70/tamil_ocr
Keywords: ocr,tamil,indian ocr,tamil ocr
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Requires-Python: >=3.9
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: torch==2.1.2
Requires-Dist: torchvision==0.16.2
Requires-Dist: Pillow==10.0.0
Requires-Dist: opencv-python==4.5.4.60
Requires-Dist: Open-Tamil
Requires-Dist: numpy
Requires-Dist: pandas
Requires-Dist: gdown
Requires-Dist: pytorch-lightning==1.9.5
Requires-Dist: lmdb
Requires-Dist: matplotlib
Requires-Dist: scikit-image
Requires-Dist: timm
Requires-Dist: nltk
Provides-Extra: dev
Requires-Dist: black; extra == "dev"
Requires-Dist: bumpver; extra == "dev"
Requires-Dist: isort; extra == "dev"
Requires-Dist: pip-tools; extra == "dev"
Requires-Dist: pytest; extra == "dev"

# OCR Tamil

<p align="center">
  <a href="License">
    <img src="https://github.com/gnana70/tamil_ocr/raw/main/test_images/MIT.svg" alt="LICENSE">
  </a>
</p>


<div align="center">
  <p>
    <a href="https://github.com/gnana70/tamil_ocr">
    <img width="50%" src="https://github.com/gnana70/tamil_ocr/raw/main/test_images/logo.gif">
    </a>
  </p>

</div>

Finetuned version of PARSEQ model on Tamil text. This version of OCR is much more robust to tilted text compared to the Tesseract, Paddle OCR and Easy OCR. This model is work in progress, feel free to contribute!!!

Currently supports two languages (English + Tamil). Accuracy of the model can be improved by adjusting the Text detection model as per your requirements. Achieved the accuracy of around **>95%** (98% NED) in validation set

## OUTPUT

 Input Image                                                                |  OCR TAMIL            | Tesseract         | 
|:--------------------------------------------------------------------------:|:--------------------:|:-----------------:|
| <img width="200" alt="teaser" src="https://github.com/gnana70/tamil_ocr/raw/main/test_images/4.jpg">                   | வாழ்கவளமுடன்     |    க்‌ க்கஸாரகளள௮ஊகஎளமுடன்‌    | 
| <img width="200" alt="teaser" src="https://github.com/gnana70/tamil_ocr/raw/main/test_images/10.jpg">                  | ரெடிமேட்ஸ்          |**NO OUTPUT**      | 
| <img width="200" alt="teaser" src="https://github.com/gnana70/tamil_ocr/raw/main/test_images/2.jpg">                   | கோபி               | **NO OUTPUT**            | 
| <img width="200" alt="teaser" src="https://github.com/gnana70/tamil_ocr/raw/main/test_images/6.jpg">                   | தாம்பரம்            | **NO OUTPUT** | 
| <img width="200" alt="teaser" src="https://github.com/gnana70/tamil_ocr/raw/main/test_images/1.jpg">                   | நெடுஞ்சாலைத்      | **NO OUTPUT**             | 

**Obtained Tesseract results using the [huggingface space](https://huggingface.co/spaces/kneelesh48/Tesseract-OCR) with Tamil as language**

## How to USE this repo

**Tested using Python 3.10 on Windows Machine**
### Pip
1. Using PIP install 
```pip install ocr_tamil```
2. Download the model weights from from the [GDRIVE](https://drive.google.com/drive/folders/1oMxdp7VE4Z0uHQkHr1VIrXYfyjZ_WwFV?usp=sharing) and keep it in the local folder to use in step 4
3. Use the below code for text recognition at word level

**Text Recognition**
```python
from ocr_tamil.ocr import OCR
image_path = r"test_images\1.jpg" # insert your own path here (step 2 file location)
model_path = r"parseq_tamil_v6.ckpt" # add the full path of the model(step 2 file location)
ocr = OCR(tamil_model_path=model_path)
texts = ocr.predict(image_path)
with open("output.txt","w",encoding="utf-8") as f:
    f.write(texts)

>>>> நெடுஞ்சாலைத்
```


**Text Detect + Recognition**

```python
from ocr_tamil.ocr import OCR
image_path = r"test_images\0.jpg" # insert your own path here
model_path = r"parseq_tamil_v6.ckpt" # add the full path of the parseq model
text_detect_model = "craft_mlt_25k.pth" # add the full path of the craft model
ocr = OCR(detect=True,tamil_model_path=model_path,detect_model_path=text_detect_model)
texts = ocr.predict(image_path)
with open("output.txt","w",encoding="utf-8") as f:
    f.write(texts)

>>>> கொடைக்கானல் Kodaikanal 

```


### Github
1. Clone the repository
2. Pip install the required modules using
3. Download the models weights from the [GDRIVE](https://drive.google.com/drive/folders/1oMxdp7VE4Z0uHQkHr1VIrXYfyjZ_WwFV?usp=sharing) and keep it under model_weights 
    
        |___model_weights
            |_____craft_mlt_25k.pth
            |_____parseq_tamil_v6.ckpt
    
4. Run the below code by providing the path 

**Text Recognition**

```python
from ocr_tamil.ocr import OCR

image_path = r"test_images\1.jpg" # insert your own path here
ocr = OCR()
texts = ocr.predict(image_path)
with open("output.txt","w",encoding="utf-8") as f:
    f.write(texts)

>>>> நெடுஞ்சாலைத்

```

**Text Detect + Recognition**

```python
from ocr_tamil.ocr import OCR

image_path = r"test_images\0.jpg" # insert your own path here
ocr = OCR(detect=True)
texts = ocr.predict(image_path)
with open("output.txt","w",encoding="utf-8") as f:
    f.write(texts)

>>>> கொடைக்கானல் Kodaikanal 

```

## LIMITATIONS

Unable to read the text if they are present in rotated forms

<img width="200" alt="teaser" src="https://github.com/gnana70/tamil_ocr/raw/main/test_images/8.jpg"> 
<img width="200" alt="teaser" src="https://github.com/gnana70/tamil_ocr/raw/main/test_images/9.jpg">


## Thanks to the below contibuters for making awesome Text detection and text recognition models

**Text detection** - [CRAFT TEXT DECTECTION](https://github.com/clovaai/CRAFT-pytorch)

**Text recognition** - [PARSEQ](https://github.com/baudm/parseq)


```bibtex
@InProceedings{bautista2022parseq,
  title={Scene Text Recognition with Permuted Autoregressive Sequence Models},
  author={Bautista, Darwin and Atienza, Rowel},
  booktitle={European Conference on Computer Vision},
  pages={178--196},
  month={10},
  year={2022},
  publisher={Springer Nature Switzerland},
  address={Cham},
  doi={10.1007/978-3-031-19815-1_11},
  url={https://doi.org/10.1007/978-3-031-19815-1_11}
}
```

```bibtex
@inproceedings{baek2019character,
  title={Character Region Awareness for Text Detection},
  author={Baek, Youngmin and Lee, Bado and Han, Dongyoon and Yun, Sangdoo and Lee, Hwalsuk},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
  pages={9365--9374},
  year={2019}
}
```

## CITATION

```bibtex
@InProceedings{GnanaPrasath,
  title={Tamil OCR},
  author={Gnana Prasath D},
  month={01},
  year={2024},
  url={https://github.com/gnana70/tamil_ocr}
}
```
