docTR vs Tesseract OCR
docTR
mindee.github.ioTesseract OCR
github.comSide by side
| Figure | docTR | Tesseract OCR |
|---|---|---|
| Price from | Free | Free |
| Free plan | Yes | Yes |
| Free trial | — | — |
| Open source | — | — |
| Windows | Yes | Yes |
| macOS | Yes | Yes |
| Linux | Yes | Yes |
| Android | — | Yes |
| API | Yes | Yes |
| Self-hosted | Yes | Yes |
| Plans published | 1 | 1 |
| Spec score · OCR software | 9.0 (#3) | 9.1 (#2) |
| Founded | 2021 | 1985 |
| Free plan | Yes | Yes |
| Input formats | docTR can read PDFs, images, and web pages, with HTML input requiring the optional html extra. | The project lists PNG, JPEG, and TIFF among its supported image formats. |
| Supported inputs | both | image |
| Installation | The library can be installed with pip or from Git, and official Docker images are available from GitHub Container Registry. | The documentation describes installation through Linux distributions, macOS package managers, Windows installers, and source builds. |
| License | The documentation's source code identifies the program as licensed under Apache License 2.0. | The code is licensed under Apache License 2.0 and is provided without warranties under the license terms. |
| 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. | Tesseract is an OCR engine and command-line program for extracting printed text from images. |
| Support | The contribution guide directs users with questions to GitHub Discussions and also mentions a #doctr channel on Slack. | The README directs users to documentation, FAQs, user and developer forums, past issues, and mailing lists, and says issues should be used for bug reports rather than questions. |
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.
Tesseract OCR
Tesseract OCR is a free, open-source OCR engine and command-line program for extracting printed text from images. It accepts image inputs including PNG, JPEG, and TIFF, and can output plain text, hOCR, PDF, TSV, ALTO, and PAGE formats. PDF options include searchable PDFs and invisible-text-only PDFs. Tesseract 4 and later include an LSTM-based engine focused on line recognition, while Legacy OCR Engine mode retains compatibility with Tesseract 3. The project says it recognizes more than 100 languages out of the box; language data is installed separately from the engine. Developers can build applications with the C or C++ libtesseract API, and the documentation lists wrappers for other languages. The repository code uses the Apache License 2.0. Tesseract does not include a graphical user interface and points users to third-party interfaces. It uses Leptonica to open images, and improving input image quality may be needed for better recognition results. Installation instructions cover Linux, macOS, and Windows, with listed platforms also including Android and self-hosting.