comiq 0.1.1

Comic-Focused Hybrid OCR Python Library

ComiQ: Comic-Focused Hybrid OCR Library

ComiQ is an advanced Optical Character Recognition (OCR) library specifically designed for comics. It combines traditional OCR engines with a powerful AI model to provide accurate text detection and grouping in comic images.

For examples of ComiQ's capabilities, visit the examples directory.

Features

• Hybrid OCR: Combines multiple OCR engines for improved accuracy and robustness.
• Extensible: Register your own custom OCR engines.
• AI-Powered Grouping: Uses an AI model to intelligently group detected text into coherent bubbles and captions.
• Configurable: Easily configure the AI model, OCR engines, and other parameters.
• Flexible: Supports both file paths and in-memory image arrays.
• Environment-Friendly: Automatically loads your API key from environment variables.

Installation

You can install ComiQ and its dependencies with a single pip command:

pip install comiq

This will install the CPU-versions of the required OCR libraries. For GPU acceleration, please see the section below.

Optional: GPU Acceleration

For significantly better performance, you can install the GPU-enabled versions of PyTorch and PaddlePaddle. It is highly recommended to do this in a clean virtual environment.

1. Uninstall CPU Versions (if necessary): If you have already installed the CPU versions, uninstall them first:

pip uninstall torch paddlepaddle

2. Install GPU-Enabled PyTorch: Visit the official PyTorch website and use the command generator to find the correct installation command for your specific system (OS, package manager, and CUDA version).

Example for Windows with an NVIDIA GPU (CUDA 12.1):

pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121

3. Install GPU-Enabled PaddlePaddle:

pip install paddlepaddle-gpu

After installing the GPU versions, the OCR engines in ComiQ will automatically use them.

Quick Start

1. Set your API Key: ComiQ requires a Gemini API key. You can either pass it to the ComiQ constructor or set it as an environment variable named GEMINI_API_KEY.

   You can create a .env file in your project's root directory:

   GEMINI_API_KEY="your-api-key-here"
   

2. Use the ComiQ class:

from comiq import ComiQ
import cv2

# Initialize ComiQ. It will automatically load the API key from the .env file.
comiq = ComiQ()

# Process an image from a file path
image_path = "path/to/your/comic/image.jpg"
data = comiq.extract(image_path)

# Or process an image from a numpy array
image_array = cv2.imread(image_path)
data_from_array = comiq.extract(image_array)

# 'data' now contains a list of text bubbles with their text and locations.
print(data)

API Reference

ComiQ(api_key: str = None, model_name: str = "gemini-1.5-flash", **kwargs)

Initializes the ComiQ instance.

• api_key (str, optional): Your Gemini API key. If not provided, it will be loaded from the GEMINI_API_KEY environment variable.
• model_name (str, optional): The name of the AI model to use. Defaults to "gemini-1.5-flash".
• **kwargs: Additional configuration for the OCR and AI models. See "Custom Configuration" for more details.

extract(image: Union[str, 'numpy.ndarray'], ocr: Union[str, List[str]] = "paddleocr")

Extracts and groups text from the given comic image.

• image (str or numpy.ndarray): The path to the image file or the image as a NumPy array.
• ocr (str or list, optional): The OCR engine(s) to use. Can be "paddleocr", "easyocr", or a list like ["paddleocr", "easyocr"]. Defaults to "paddleocr".

Returns:

• dict: A dictionary containing the processed data, including text bubbles, their locations, and other metadata.

register_ocr_engine(name: str, engine: Callable)

Registers a new OCR engine.

• name (str): The name to identify the engine.
• engine (Callable): The function that implements the engine. See "Advanced Usage" for details.

get_available_ocr_engines() -> List[str]

Returns a list of all registered OCR engine names.

Advanced Usage: Registering a Custom OCR Engine

You can extend ComiQ by adding your own OCR engine. Your custom engine must be a function that adheres to the following contract:

• Input: It must accept two arguments:
  1. image (a numpy.ndarray in BGR format).
  2. **kwargs (a dictionary for any configuration your engine needs).
• Output: It must return a list of dictionaries, where each dictionary represents a detected text box and has two keys:
  1. "text_box": A list of four integers [ymin, xmin, ymax, xmax].
  2. "text": The detected text as a string.

Example:

Here is how you could wrap the pytesseract library as a custom engine:

import comiq
import pytesseract
import cv2
import numpy as np

# 1. Define the custom engine function
def pytesseract_engine(image: np.ndarray, **kwargs) -> list:
    """
    A custom OCR engine using pytesseract.
    It expects a 'config' kwarg, e.g., pytesseract_engine(image, config="--psm 6")
    """
    # pytesseract works with RGB images
    rgb_image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
    
    # Use pytesseract to get structured data
    data = pytesseract.image_to_data(
        rgb_image,
        output_type=pytesseract.Output.DICT,
        config=kwargs.get("config", "")
    )
    
    results = []
    for i in range(len(data['text'])):
        if int(data['conf'][i]) > 60: # Filter by confidence
            (x, y, w, h) = (data['left'][i], data['top'][i], data['width'][i], data['height'][i])
            text = data['text'][i]
            if text.strip():
                results.append({
                    "text_box": [y, x, y + h, x + w],
                    "text": text
                })
    return results

# 2. Register the new engine with ComiQ
comiq.register_ocr_engine("pytesseract", pytesseract_engine)

# 3. Now you can use it!
my_comiq = comiq.ComiQ()
tesseract_config = {"ocr": {"pytesseract": {"config": "--psm 6"}}}
data = my_comiq.extract(
    "path/to/image.png",
    ocr="pytesseract",
    **tesseract_config
)

print(comiq.get_available_ocr_engines())
# Output: ['paddleocr', 'easyocr', 'pytesseract']

Custom Configuration

You can pass additional configuration to the ComiQ constructor to customize the behavior of the OCR and AI models.

from comiq import ComiQ

# Custom configuration
config = {
    "ocr": {
        "paddleocr": {
            "lang": "japan", # Use Japanese language model for PaddleOCR
        },
        "easyocr": {
            "reader": {"gpu": False}, # Disable GPU for EasyOCR
        }
    },
    "ai": {
        "temperature": 0.5, # Make the AI model more creative
    }
}

comiq = ComiQ(model_name="gemini-1.5-pro", **config)

data = comiq.extract("path/to/manga.jpg", ocr="paddleocr")

Contributing

Contributions are welcome! Please see our Contributing Guide for more details.

License

ComiQ is licensed under the MIT License.