pdf-ocr-installer

To automate setting up a PDF-OCR environment, you can run an installer script that uses uv to create a Python 3.11 virtual environment, downloads PP-OCRv5 models from Hugging Face, and generates the required dictionary file. This fully provisions dependencies and configures CUDA-enabled PyTorch if available.

You can generate the ppocr_keys_v1.txt dictionary file by extracting character definitions from a YAML configuration. The automated setup process parses the YAML file and outputs the ready-to-use dictionary file required for your OCR pipelines.

Yes, you can use uv to provision a Python 3.11 virtual environment for Hugging Face OCR models. The setup process specifically uses uv to create the virtual environment and huggingface-hub to download the PP-OCRv5 models automatically.

Yes, the PDF-OCR setup script configures CUDA-enabled PyTorch automatically. During the environment provisioning process, it detects and installs CUDA-enabled PyTorch if available to ensure the OCR pipeline is ready for inference.

The best way to reproduce a CI-like PDF-OCR build with PP-OCRv5 models is to execute an automated installer script. It handles end-to-end workflows by creating the virtual environment with uv, downloading models from Hugging Face, and generating the dictionary file.

You need a ppocr_keys_v1.txt dictionary file for your OCR pipeline because it provides the character mappings required by the PP-OCRv5 models during inference. The setup script generates this file from a YAML configuration to ensure accurate text recognition.