docTR vs OCR4all
docTR
mindee.github.ioOCR4all
ocr4all.orgSide by side
| Figure | docTR | OCR4all |
|---|---|---|
| Price from | Free | Free |
| Free plan | Yes | Yes |
| Free trial | — | — |
| Open source | — | — |
| Web | — | Yes |
| Windows | Yes | Yes |
| macOS | Yes | Yes |
| Linux | Yes | Yes |
| API | Yes | — |
| Self-hosted | Yes | Yes |
| Plans published | 1 | 1 |
| Spec score · OCR software | 9.0 (#3) | 8.0 (#22) |
| Deployment | The documentation offers templates for a browser demo and API deployment, and documents running docTR on AWS Lambda. | OCR4all runs in Docker and is accessed through a browser at a local address in the Quickstart instructions. |
| Founded | 2021 | 2019 |
| Free plan | Yes | Yes |
| Supported inputs | both | both |
| License | The documentation's source code identifies the program as licensed under Apache License 2.0. | OCR4all is released under the MIT License. |
| Primary platform | desktop | both |
| Purpose | docTR is a deep learning OCR library for locating and recognizing text in documents, intended for document automation and research. | OCR4all provides a workflow for recognizing historical printed and handwritten material, with user interaction at workflow stages to improve results. |
| Support | The contribution guide directs users with questions to GitHub Discussions and also mentions a #doctr channel on Slack. | The team page identifies a user support contact, Florian Langhanki, with a University of Würzburg email address. |
Plans
In their own words
docTR
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.