docTR vs OCRmyPDF

docTR is a free deep-learning OCR library for finding and recognizing text in documents, aimed at document automation and research. Its pretrained OCR predictors first detect text and then recognize it, while a layout predictor identifies regions such as tables, figures, and headers. It reads PDFs and images; web-page input requires the optional HTML extra. Results can be rendered as plain text or exported as a JSON-serializable nested dictionary. The project describes its predictors as optimized for CPU and GPU inference. Users can train custom detection, recognition, layout, and table-structure models when pretrained models are not suitable. Documentation covers loading and sharing models through the Hugging Face Hub, and includes a minimal REST API deployment template and a browser demo. Installation is available through pip, Git, or official Docker images. The library requires Python 3.11 or higher, and pretrained weights download on first use and are cached locally. For AWS Lambda, the guide advises disabling multiprocessing and placing the model cache within /tmp. The project uses the Apache License 2.0.

OCRmyPDF is free software that adds searchable text to scanned PDF files while retaining the original PDF as much as possible. It uses Tesseract to recognize text in page images and produces PDF/A-2b by default, with regular PDF output available as an alternative. Image-processing options such as deskew can adjust pages to support visual quality and OCR accuracy. When a PDF already contains text, its processing modes can stop with an error, skip affected pages, redo OCR, or force OCR across all pages. OCRmyPDF can also be used as a Python library, and plugins can customize processing steps. Installation methods are documented for Linux, macOS, Windows, FreeBSD, and Docker; Paperless-ngx and Nextcloud OCR are named as third-party integrations. Results may be poor for low-quality scans or languages not specified for processing, and handwriting is not recognized. The project also notes that accuracy may trail commercial solutions. Its documentation advises processing only PDFs users trust and warns that its example Docker web service has no security measures and is not meant for public internet deployment.