---
license: mit
language:
- ta
- en
metrics:
- accuracy
pipeline_tag: image-to-text
tags:
- ocr
- optical character recognition
- text recognition
- tamil
---
<h1 align="center"> OCR Tamil - Easy, Accurate and Simple to use Tamil OCR - (ஒளி எழுத்துணரி)</h1>

<p align="center">❤️️❤️️Please star✨ it if you like❤️️❤️️</p>

<p align="center">
  <a href="LICENSE">
    <img src="https://github.com/gnana70/tamil_ocr/raw/main/test_images/MIT.svg" alt="LICENSE">
  </a>
  <a href="https://huggingface.co/spaces/GnanaPrasath/ocr_tamil">
    <img src="https://github.com/gnana70/tamil_ocr/raw/main/test_images/huggingface.svg" alt="HuggingSpace">
  </a>
   <a href="https://colab.research.google.com/drive/11QPPj3EmpoIqnpuIznKeP1icxvVOjfux?usp=sharing">
    <img src="https://github.com/gnana70/tamil_ocr/raw/main/test_images/colab.svg" alt="colab">
  </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_1.gif">
    </a>
  </p>
</div>

 OCR Tamil can help you extract text from signboard, nameplates, storefronts etc., from Natural Scenes with high accuracy. This version of OCR is much more robust to tilted text compared to the Tesseract, Paddle OCR and Easy OCR as they are primarily built to work on the documents texts and not on natural scenes. This model is work in progress, feel free to contribute!!!

## Languages Supported 🔛
**➡️ English**

**➡️ Tamil (தமிழ்)**

## Accuracy 🎯
✔️ English > 98%

✔️ Tamil > 95%

## Comparison between Tesseract OCR and OCR Tamil ⚖️

 Input Image                                                                |  OCR TAMIL   🏆         | Tesseract         | EasyOCR |
|:--------------------------------------------------------------------------:|:--------------------:|:-----------------:|:-----------------:|
| <img width="200" alt="teaser" src="https://github.com/gnana70/tamil_ocr/raw/main/test_images/4.jpg">                   | வாழ்கவளமுடன்✅     |    க்‌ க்கஸாரகளள௮ஊகஎளமுடன்‌ ❌  | வாழக வளமுடன்❌|
| <img width="200" alt="teaser" src="https://github.com/gnana70/tamil_ocr/raw/main/test_images/11.jpg">                   | தமிழ்வாழ்க✅      | **NO OUTPUT** ❌           | தமிழ்வாழ்க✅      |
| <img width="200" alt="teaser" src="https://github.com/gnana70/tamil_ocr/raw/main/test_images/2.jpg">                   | கோபி ✅              | **NO OUTPUT** ❌          | ப99❌          |
| <img width="200" alt="teaser" src="https://github.com/gnana70/tamil_ocr/raw/main/test_images/6.jpg">                   | தாம்பரம் ✅           | **NO OUTPUT** ❌ | தாம்பரம❌ |
| <img width="200" alt="teaser" src="https://github.com/gnana70/tamil_ocr/raw/main/test_images/1.jpg">                   | நெடுஞ்சாலைத் ✅      | **NO OUTPUT** ❌             |நெடுஞ்சாலைத் ✅      |
| <img width="200" alt="teaser" src="https://github.com/gnana70/tamil_ocr/raw/main/test_images/5.jpg">                   | அண்ணாசாலை ✅     | **NO OUTPUT** ❌            |  ல@I9❌            |
| <img width="200" alt="teaser" src="https://github.com/gnana70/tamil_ocr/raw/main/test_images/10.jpg">                  | ரெடிமேடஸ் ❌         |**NO OUTPUT** ❌     | ரெடிமேடஸ் ❌         |

**Obtained Tesseract and EasyOCR results using the [Colab notebook](https://colab.research.google.com/drive/1ylZm6afur85Pe6I10N2_tzuBFl2VIxkW?usp=sharing) with Tamil and english as language**


## How to Install and Use OCR Tamil 👨🏼‍💻

### Quick links🌐
📔 Detailed explanation on [Medium article](https://gnana70.medium.com/ocr-tamil-easy-accurate-and-simple-to-use-tamil-ocr-b03b98697f7b). 

✍️ Experiment in [Colab notebook](https://colab.research.google.com/drive/11QPPj3EmpoIqnpuIznKeP1icxvVOjfux?usp=sharing)

🤗 Test it in [Huggingface spaces](https://huggingface.co/spaces/GnanaPrasath/ocr_tamil)


### Pip install instructions🐍
In your command line, run the following command ```pip install ocr_tamil```

If you are using jupyter notebook , install like ```!pip install ocr_tamil```

### Python Usage - Single image inference

**Text Recognition only**

```python
from ocr_tamil.ocr import OCR

image_path = r"test_images\1.jpg" # insert your own path here
ocr = OCR()
text_list = ocr.predict(image_path)
print(text_list[0])

## OUTPUT : நெடுஞ்சாலைத்
```
<img width="200" alt="teaser" src="https://github.com/gnana70/tamil_ocr/raw/main/test_images/1_180.jpg">


**Text Detect + Recognition**

```python
from ocr_tamil.ocr import OCR

image_path = r"test_images\0.jpg" # insert your own image path here
ocr = OCR(detect=True)
texts = ocr.predict(image_path)
print(text_list[0])

## OUTPUT : கொடைக்கானல் Kodaikanal 

```
<img width="400" alt="teaser" src="https://github.com/gnana70/tamil_ocr/raw/main/test_images/0.jpg">


### Batch inference mode 💻

**Text Recognition only**

```python
from ocr_tamil.ocr import OCR

image_path = [r"test_images\1.jpg",r"test_images\2.jpg"] # insert your own image paths here
ocr = OCR()
text_list = ocr.predict(image_path)

for text in text_list:
    print(text)

## OUTPUT : நெடுஞ்சாலைத்
## OUTPUT : கோபி

```

**Text Detect + Recognition**

```python
from ocr_tamil.ocr import OCR

image_path = [r"test_images\0.jpg",r"test_images\tamil_sentence.jpg"] # insert your own image paths here
ocr = OCR(detect=True)
text_list = ocr.predict(image_path)

for text in text_list:
    print(text)

## OUTPUT : கொடைக்கானல் Kodaikanal 
## OUTPUT : செரியர் யற்கை மூலிகைகளில் இருந்து ஈர்த்தெடுக்க்கப்பட்ட வீரிய உட்பொருட்களை உள்ளடக்கி எந்த இரசாயன சேர்க்கைகளும் இல்லாமல் உருவாக்கப்பட்ட இந்தியாவின் முதல் சித்த தயாரிப்பு 

```

**Tested using Python 3.10 on Windows & Linux (Ubuntu 22.04) Machines**

## Applications⚡
1. ADAS system navigation based on the signboards + maps (hybrid approach) 🚁
2. License plate recognition 🚘

## Limitations⛔

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

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

2. Currently supports Only English and Tamil Language

3. Document Text reading capability is limited. Auto identification of Paragraph, line are not supported along with Text detection model inability to detect and crop the Tamil text leads to accuracy decrease (**WORKAROUND** Can use your own text detection model along with OCR tamil text recognition model)
<p align="center">
<img width="200" alt="teaser" src="https://github.com/gnana70/tamil_ocr/raw/main/test_images/tamil_sentence.jpg">
</p>
<p align="center">
<span>Cropped Text from Text detection Model</span>
</p>
<p align="center">
<img width="200" alt="teaser" src="https://github.com/gnana70/tamil_ocr/raw/main/test_images/tamil_sentence_crop.jpg">
</p>
<p align="center">
Character **இ** missing due to text detection model error 
</p>

**?**யற்கை மூலிகைகளில் இருந்து ஈர்த்தெடுக்கக்கப்பட்ட வீரிய உட்பொருட்களை உள்ளடக்கி எந்த இரசாயன சேர்க்கைகளும் இல்லாமல் உருவாக்கப்பட்ட இந்தியாவின் முதல் சித்த தயாரிப்பு 


## Acknowledgements 👏

**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}
}
```

![logo](https://github.com/gnana70/tamil_ocr/raw/main/test_images/logo_1.gif)