docTR vs SimpleOCR
docTR
mindee.github.io
SimpleOCR
simpleocr.comSide by side
| Figure | docTR | SimpleOCR |
|---|---|---|
| Price from | Free | Free |
| Free plan | Yes | Yes |
| Free trial | — | — |
| Open source | — | — |
| Windows | Yes | Yes |
| macOS | Yes | — |
| Linux | Yes | — |
| API | Yes | — |
| Self-hosted | Yes | — |
| Plans published | 1 | 1 |
| Spec score · OCR software | 9.0 (#3) | 7.6 (#33) |
| Founded | 2021 | 1999 |
| Free plan | Yes | Yes |
| Supported inputs | both | image |
| Maker | The project documentation says docTR is actively maintained by Mindee. | The site identifies Meta Enterprises, LLC in Knoxville, Tennessee, as the company behind the site. |
| 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. | SimpleOCR converts printed paper documents or images into editable digital text using optical character recognition. |
| Support | The contribution guide directs users with questions to GitHub Discussions and also mentions a #doctr channel on Slack. | The maker offers access to OCR specialists and invites users to contact its team for help with OCR projects. |
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.