sinapsis-ocr 0.1.10

Implements Sinapsis templates to perform optical character recognition on images

Sinapsis OCR

Templates for Optical Character Recognition (OCR) in images or PDFs

🐍 Installation • 📦 Packages • 🚀 Features • 📚 Usage example • 🌐 Webapp • 📙 Documentation • 🔍 License

Sinapsis OCR provides powerful and flexible implementations for extracting text from images using different OCR engines. It enables users to easily configure and run OCR tasks with minimal setup.

🐍 Installation

This mono repo consists of different packages for OCR:

• sinapsis-doctr
• sinapsis-easyocr

Install using your package manager of choice. We encourage the use of uv

Example with uv:

  uv pip install sinapsis-doctr --extra-index-url https://pypi.sinapsis.tech

or with raw pip:

  pip install sinapsis-doctr --extra-index-url https://pypi.sinapsis.tech

Change the name of the package for the one you want to install.

[!IMPORTANT] Templates in each package may require extra dependencies. For development, we recommend installing the package with all the optional dependencies:

with uv:

  uv pip install sinapsis-doctr[all] --extra-index-url https://pypi.sinapsis.tech

or with raw pip:

  pip install sinapsis-doctr[all] --extra-index-url https://pypi.sinapsis.tech

[!TIP] You can also install all the packages within this project:

  uv pip install sinapsis-ocr[all] --extra-index-url https://pypi.sinapsis.tech

📦 Packages

Packages summary

• Sinapsis DocTR

  • Uses the DocTR library for high-quality OCR with modern deep learning models
  • Supports multiple detection and recognition architectures
  • Provides detailed text extraction with bounding boxes and confidence scores

• Sinapsis EasyOCR

  • Leverages the EasyOCR library for simple yet effective OCR
  • Supports multiple languages
  • Extracts text with bounding boxes and confidence scores

[!TIP] Use CLI command sinapsis info --all-template-names to show a list with all the available Template names installed with Sinapsis OCR.

[!TIP] Use CLI command sinapsis info --example-template-config TEMPLATE_NAME to produce an example Agent config for the Template specified in TEMPLATE_NAME.

For example, for DocTROCRPrediction use sinapsis info --example-template-config DocTROCRPrediction to produce an example config.

📚 Usage example

DocTR Example

agent:
  name: doctr_prediction
  description: agent to run inference with DocTR, performs on images read, recognition and save

templates:
- template_name: InputTemplate
  class_name: InputTemplate
  attributes: {}

- template_name: FolderImageDatasetCV2
  class_name: FolderImageDatasetCV2
  template_input: InputTemplate
  attributes:
    data_dir: dataset/input

- template_name: DocTROCRPrediction
  class_name: DocTROCRPrediction
  template_input: FolderImageDatasetCV2
  attributes:
    recognized_characters_as_labels: True

- template_name: BBoxDrawer
  class_name: BBoxDrawer
  template_input: DocTROCRPrediction
  attributes:
    draw_confidence: True
    draw_extra_labels: True

- template_name: ImageSaver
  class_name: ImageSaver
  template_input: BBoxDrawer
  attributes:
    save_dir: output
    root_dir: dataset

EasyOCR Example

agent:
  name: easyocr_inference
  description: agent to run inference with EasyOCR, performs on images read, recognition and save

templates:
- template_name: InputTemplate
  class_name: InputTemplate
  attributes: {}

- template_name: FolderImageDatasetCV2
  class_name: FolderImageDatasetCV2
  template_input: InputTemplate
  attributes:
    data_dir: dataset/input

- template_name: EasyOCR
  class_name: EasyOCR
  template_input: FolderImageDatasetCV2
  attributes: {}

- template_name: BBoxDrawer
  class_name: BBoxDrawer
  template_input: EasyOCR
  attributes:
    draw_confidence: True
    draw_extra_labels: True

- template_name: ImageSaver
  class_name: ImageSaver
  template_input: BBoxDrawer
  attributes:
    save_dir: output
    root_dir: dataset

To run, simply use:

sinapsis run name_of_the_config.yml

🌐 Webapp

The webapp provides a simple interface to extract text from images using OCR. Upload your image, and the app will process it and display the detected text with bounding boxes.

[!IMPORTANT] To run the app you first need to clone this repository:

git clone https://github.com/Sinapsis-ai/sinapsis-ocr.git
cd sinapsis-ocr

[!NOTE] If you'd like to enable external app sharing in Gradio, export GRADIO_SHARE_APP=True

[!TIP] The agent configuration can be updated using the AGENT_CONFIG_PATH environment var. For default uses the config for easy ocr but this can be chaged with: AGENT_CONFIG_PATH=/app/packages/sinapsis_doctr/src/sinapsis_doctr/configs/doctr_demo.yaml

🐳 Docker

IMPORTANT This docker image depends on the sinapsis:base image. Please refer to the official sinapsis instructions to Build with Docker.

1. Build the sinapsis-ocr image:

docker compose -f docker/compose.yaml build

2. Start the app container:

docker compose -f docker/compose_app.yaml up

3. Check the status:

docker logs -f sinapsis-ocr-app

4. The logs will display the URL to access the webapp, e.g.:

NOTE: The url can be different, check the output of logs

Running on local URL:  http://127.0.0.1:7860

5. To stop the app:

docker compose -f docker/compose_app.yaml down

💻 UV

To run the webapp using the uv package manager, please:

1. Create the virtual environment and sync the dependencies:

uv sync --frozen

2. Install packages:

uv pip install sinapsis-ocr[all] --extra-index-url https://pypi.sinapsis.tech

3. Run the webapp:

uv run webapps/gradio_ocr.py

4. The terminal will display the URL to access the webapp, e.g.:

Running on local URL:  http://127.0.0.1:7860

NOTE: The url can be different, check the output of the terminal

5. To stop the app press Control + C on the terminal

📙 Documentation

Documentation for this and other sinapsis packages is available on the sinapsis website

Tutorials for different projects within sinapsis are available at sinapsis tutorials page

🔍 License

This project is licensed under the AGPLv3 license, which encourages open collaboration and sharing. For more details, please refer to the LICENSE file.

For commercial use, please refer to our official Sinapsis website for information on obtaining a commercial license.