docTR vs Transym TOCR

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.

Transym TOCR has no write-up on Specifiction yet; its figures are above.