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Swift OCR: LLM Powered Fast OCR ⚡

🌟 Features

  • Flexible Input Options: Accepts PDF files via direct upload or by specifying a URL.
  • Advanced OCR Processing: Utilizes OpenAI's GPT-4 Turbo with Vision model for accurate text extraction.
  • Performance Optimizations:
    • Parallel PDF Conversion: Converts PDF pages to images concurrently using multiprocessing.
    • Batch Processing: Processes multiple images in batches to maximize throughput.
    • Retry Mechanism with Exponential Backoff: Ensures resilience against transient failures and API rate limits.
  • Structured Output: Extracted text is formatted using Markdown for readability and consistency.
  • Robust Error Handling: Comprehensive logging and exception handling for reliable operations.
  • Scalable Architecture: Asynchronous processing enables handling multiple requests efficiently.

📹 Demo

video.mp4

Demo video showcasing the conversion of NASA's Apollo 17 flight documents, which include unorganized, horizontally and vertically oriented pages, into well-structured Markdown format without any issues.

Here's a single, comprehensive section on cost comparison for your README:

Cost Comparison and Value Proposition

Our solution offers an optimal balance of affordability, accuracy, and advanced features:

Cost Breakdown

  • Average token usage per image: ~1200
  • Total tokens per page (including prompt): ~1500
  • [GPT4O] Input token cost: $5 per million tokens
  • [GPT4O] Output token cost: $15 per million tokens

For 1000 documents:

  • Estimated total cost: $15

Cost Optimization Options

  1. Utilizing GPT4 mini: Reduces cost to ~$8 per 1000 documents
  2. Implementing batch API: Further reduces cost to ~$4 per 1000 documents

Market Comparison

This solution is significantly more affordable than alternatives:

  • Our cost: $15 per 1000 documents
  • CloudConvert: ~$30 per 1000 documents (PDFTron mode, 4 credits required)

While cost-effectiveness is a major advantage, our solution also provides:

  • Superior accuracy and consistency
  • Precise table generation
  • Output in easily editable markdown format

This combination of affordability and advanced features makes solution stand out in the document processing market. It's not just about being cheaper; it's about providing excellent value through reliability, flexibility, and high-quality output.

🛠️ Installation

Prerequisites

Steps

  1. Clone the Repository

    git clone https://github.com/yigitkonur/llm-openai-ocr.git
cd llm-openai-ocr

  2. Create a Virtual Environment

    python3 -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

  3. Install Dependencies

    pip install -r requirements.txt

  4. Configure Environment Variables

    Create a .env file in the root directory and add the following variables:

    OPENAI_API_KEY=your_openai_api_key
AZURE_OPENAI_ENDPOINT=your_azure_openai_endpoint
OPENAI_DEPLOYMENT_ID=your_openai_deployment_id
OPENAI_API_VERSION=your_openai_api_version  # Default is "gpt-4o"
BATCH_SIZE=10  # Optional: Default is 1
MAX_CONCURRENT_OCR_REQUESTS=5  # Optional: Default is 5
MAX_CONCURRENT_PDF_CONVERSION=4  # Optional: Default is 4

    Note: Replace your_openai_api_key, your_azure_openai_endpoint, and your_openai_deployment_id with your actual OpenAI credentials.

  5. Run the Application

    uvicorn main:app --reload

    The API will be available at http://127.0.0.1:8000.

🎯 Usage

API Endpoint

POST /ocr

Request Parameters

  • file: (Optional) Upload a PDF file.
  • ocr_request.url: (Optional) URL of the PDF to process.

You must provide either a file or a URL, not both.

Example Using curl

Uploading a PDF File:

curl -X POST "http://127.0.0.1:8000/ocr" -F "file=@/path/to/your/document.pdf"

Providing a PDF URL:

curl -X POST "http://127.0.0.1:8000/ocr" -F "ocr_request={\"url\": \"https://example.com/document.pdf\"}" -H "Content-Type: application/json"

Response

  • 200 OK

    {
  "text": "Extracted and formatted text from the PDF."
}

  • Error Responses

    • 400 Bad Request: Invalid input parameters.
    • 422 Unprocessable Entity: Validation errors.
    • 500 Internal Server Error: Processing errors.

🧰 Configuration

All configurations are managed via environment variables. Ensure you have a .env file set up with the necessary variables as described in the Installation section.

Key Configuration Variables

  • OPENAI_API_KEY: Your OpenAI API key.
  • AZURE_OPENAI_ENDPOINT: The endpoint for Azure OpenAI service.
  • OPENAI_DEPLOYMENT_ID: Deployment ID for the OpenAI model.
  • OPENAI_API_VERSION: API version for OpenAI (default: "gpt-4o").
  • BATCH_SIZE: Number of images to process per OCR request (default: 1).
  • MAX_CONCURRENT_OCR_REQUESTS: Maximum number of concurrent OCR requests (default: 5).
  • MAX_CONCURRENT_PDF_CONVERSION: Maximum number of concurrent PDF page conversions (default: 4). Here's the revised license section with the requested changes:

📜 License (thx for issue)

Please note that PyMuPDF requires changing the license to GNU AGPL v3.0. You can fork this project, implement pdf2image, and use it freely. While I don't have any particular interest in licensing, I am legally obligated to add this information.

GNU AFFERO GENERAL PUBLIC LICENSE Version 3, 19 November 2007

Copyright (C) 2024 Yiğit Konur

This program is free software: you can redistribute it and/or modify it under the terms of the GNU Affero General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Affero General Public License for more details.

You should have received a copy of the GNU Affero General Public License along with this program. If not, see https://www.gnu.org/licenses/.