docTR vs Kraken
docTR
mindee.github.ioKraken
kraken.reSide by side
| Figure | docTR | Kraken |
|---|---|---|
| Price from | Free | — |
| Free plan | Yes | — |
| Free trial | — | — |
| Open source | — | — |
| Windows | Yes | — |
| macOS | Yes | Yes |
| Linux | Yes | Yes |
| API | Yes | Yes |
| Self-hosted | Yes | — |
| Plans published | 1 | 0 |
| Spec score · OCR API Software | 8.9 (#2) | 7.5 (#22) |
| Spec score · OCR software | 9.0 (#3) | 7.3 (#36) |
| Free plan | Yes | Yes |
| Supported inputs | both | both |
| Installation | The library can be installed with pip or from Git, and official Docker images are available from GitHub Container Registry. | The documentation says Kraken can be installed on Linux or Mac OS X, on x64 and ARM, using pip or Anaconda. |
| Integrations | The documentation describes loading and sharing models through the Hugging Face Hub. | The documentation lists eScriptorium, OCR4all, the OCR-D suite, and arkindex by Teklia as applications or suites integrated with Kraken. |
| Primary platform | desktop | desktop |
| Purpose | docTR is a deep learning OCR library for locating and recognizing text in documents, intended for document automation and research. | Kraken is an open-source automatic text recognition system optimized for historical documents and non-Latin scripts. |
| Support | The contribution guide directs users with questions to GitHub Discussions and also mentions a #doctr channel on Slack. | The project welcomes feedback, pull requests, bug reports, and feature suggestions through its GitHub repository. |
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.