docTR vs Tesseract OCR

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.

Tesseract OCR is a free, open-source engine that extracts printed text from images. It can be run from the command line or used through an API; the project does not include a graphical application. It recognizes more than 100 languages out of the box and supports Unicode UTF-8. Version 4 introduced an LSTM neural-network engine for line recognition while retaining the legacy character-pattern engine. Input formats include PNG, JPEG, TIFF, JPEG 2000, GIF, WebP, BMP, and PNM. Output formats include plain text, hOCR, PDF, TSV, ALTO, and PAGE. Tesseract cannot read PDF files directly, although PDF is available as an output format; the documentation suggests converting files or using OCRmyPDF. The project documents wrappers for several languages, including Python, Java, Swift, Flutter, Ruby, Rust, and Go, as well as C and C++ APIs. The repository code uses Apache License 2.0, though dependencies may have different licenses. Image quality may need improvement for better recognition results.