ocr-tamil 0.1.1

Python Tamil OCR package

OCR Tamil - Easy, Accurate and Simple to use Tamil OCR

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!!!

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

Comparison between Tesseract OCR and OCR Tamil

Input Image	OCR TAMIL	Tesseract
	வாழ்கவளமுடன்	க்‌ க்கஸாரகளள௮ஊகஎளமுடன்‌
	ரெடிமேட்ஸ்	NO OUTPUT
	கோபி	NO OUTPUT
	தாம்பரம்	NO OUTPUT
	நெடுஞ்சாலைத்	NO OUTPUT
	அண்ணாசாலை	NO OUTPUT

Obtained Tesseract results using the huggingface space with Tamil as language

How to Install and Use OCR Tamil

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

Pip

1. Using PIP install pip install ocr_tamil
2. Download the model weights from from the GDRIVE and keep it in the local folder to use in step 3
3. Use the below code for text recognition at word level by inserting the image_path and model path

Text Recognition

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

4. Use the below code for text detection and recognition by inserting the image_path and model path s (both detection and recognition models)

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 pip install -r requirements.txt

3. Download the models weights from the GDRIVE 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

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

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 

Applications

1. Navigating autonomous vehicles based on the signboards
2. License plate recognition

Limitations

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

2. Currently supports Only English and Tamil Language

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

Text detection - CRAFT TEXT DECTECTION

Text recognition - PARSEQ

@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}
}

@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

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