Asprise OCR SDK vs docTR
Asprise OCR SDK
asprise.comdocTR
mindee.github.ioSide by side
| Figure | Asprise OCR SDK | docTR |
|---|---|---|
| Price from | — | Free |
| Free plan | — | Yes |
| Free trial | Yes | — |
| Open source | — | — |
| Windows | Yes | Yes |
| macOS | Yes | Yes |
| Linux | Yes | Yes |
| API | — | Yes |
| Self-hosted | Yes | Yes |
| Plans published | 3 | 1 |
| Spec score · OCR API Software | 8.4 (#6) | 8.9 (#2) |
| Spec score · OCR software | 7.9 (#27) | 9.0 (#3) |
| Deployment | Asprise describes the SDK as royalty-free for distribution to end users without additional deployment fees and says no licensing server is required. | The documentation offers templates for a browser demo and API deployment, and documents running docTR on AWS Lambda. |
| Free plan | No | Yes |
| Support | The Java trial download page directs users with problems to Asprise support, and the company contact page lists [email protected]. | The contribution guide directs users with questions to GitHub Discussions and also mentions a #doctr channel on Slack. |
Plans
Asprise OCR SDK
- Lite$5,998, paid once
- STD$9,998, paid once
- ENT$15,998, paid once
In their own words
Asprise OCR SDK
Asprise OCR SDK has no write-up on Specifiction yet; its figures are above.
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.