EasyOCR Document Loader¶

The EasyOCR loader uses the EasyOCR engine to extract text from images and PDF files. It's known for its ease of use and support for a wide range of languages.

Supported Formats¶

png
jpg/jpeg
tiff/tif
webp
pdf

Usage¶

Basic Usage¶

from extract_thinker import DocumentLoaderEasyOCR, EasyOCRConfig

# Initialize with default settings
config = EasyOCRConfig()
loader = DocumentLoaderEasyOCR(config)

# Load document
pages = loader.load("path/to/your/image.png")

# Process extracted content
for page in pages:
    # Access text content
    text = page["content"]
    print(text)

Configuration-based Usage¶

from extract_thinker import DocumentLoaderEasyOCR, EasyOCRConfig

# Create configuration
config = EasyOCRConfig(
    lang_list=['en', 'fr'],        # Use English and French
    gpu=True,                      # Enable GPU acceleration
    cache_ttl=600,                 # Cache results for 10 minutes
    include_bbox=True              # Include bounding box details
)

# Initialize loader with configuration
loader = DocumentLoaderEasyOCR(config)

# Load a PDF document
pages = loader.load("path/to/your/document.pdf")

# Process extracted content with details
for page in pages:
    print(f"Content: {page['content']}")
    if 'detail' in page:
        for detail in page['detail']:
            print(f"  - Text: {detail['text']}, BBox: {detail['bbox']}")

Configuration Options¶

The EasyOCRConfig class supports the following options:

Option	Type	Default	Description
`lang_list`	List[str]	`['en']`	List of language codes for OCR (e.g., `['en', 'es']`).
`gpu`	bool	`True`	Whether to use GPU for processing (if available).
`download_enabled`	bool	`True`	Automatically download language models if not found.
`cache_ttl`	int	`300`	Time-to-live for cached results, in seconds.
`include_bbox`	bool	`False`	Whether to include detailed bounding box information in the output.

Features¶

Text extraction from images and PDFs
Multi-language support with automatic model downloading
Optional inclusion of detailed bounding box data
GPU acceleration for faster processing
Caching support to improve performance for repeated requests
Local processing with no external API calls

Installation¶

EasyOCR requires the easyocr and torch libraries. You can install them using pip:

pip install easyocr torch

For GPU support, you may need to install a specific version of PyTorch that matches your CUDA version. Please refer to the official PyTorch installation guide for more details.

Notes¶

Vision mode is not supported by this loader.
Performance is significantly better when a GPU is available.
The first time a language is used, the corresponding model will be downloaded, which may take some time.