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BentoOCR


Turn any OCR models into online inference API endpoint 🚀
 Powered by BentoML 🍱

📖 Introduction 📖

This project demonstrates how to effortlessly serve an OCR model using BentoML. It accepts PDFs as input and returns the text contained within. The service uses EasyOCR for OCR.

🏃‍♂️ Running the Service 🏃‍♂️

BentoML CLI

Prerequisite 📋

✅ Python

This project requires Python 3.10 or higher.

Make sure to install uv

git clone https://github.com/bentoml/BentoOCR.git && cd BentoOCR

uv venv

python import_model.py

To serve the model with BentoML:

bentoml serve

You can then open your browser at http://127.0.0.1:3000 and interact with the service through Swagger UI.

🌐 Interacting with the Service 🌐

BentoML's default model serving method is through an HTTP server. In this section, we demonstrate various ways to interact with the service:

cURL

curl -X 'POST' \
  'http://localhost:3000/image_to_text' \
  -H 'accept: application/pdf' \
  -H 'Content-Type: multipart/form-data' \
  -F file=@path-to-pdf

Replace path-to-pdf with the file path of the PDF you want to send to the service.

Swagger UI

You can use Swagger UI to quickly explore the available endpoints of any BentoML service. Swagger UI

🚀 Deploying to Production 🚀

Effortlessly transition your project into a production-ready application using BentoCloud, the production-ready platform for managing and deploying machine learning models.

Start by creating a BentoCloud account. Once you've signed up, log in to your BentoCloud account using the command:

bentoml cloud login --api-token <your-api-token> --endpoint <bento-cloud-endpoint>

Note: Replace <your-api-token> and <bento-cloud-endpoint> with your specific API token and the BentoCloud endpoint respectively.

Next, build your BentoML service using the build command:

bentoml build

Then, push your freshly-built Bento service to BentoCloud using the push command:

bentoml push <name:version>

Lastly, deploy this application to BentoCloud with a single bentoml deployment create command following the deployment instructions.

BentoML offers a number of options for deploying and hosting online ML services into production, learn more at Deploying a Bento.

👥 Community 👥

BentoML has a thriving open source community where thousands of ML/AI practitioners are contributing to the project, helping other users and discussing the future of AI. 👉 Pop into our Slack community!