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Asprise OCR SDK vs docTR

Asprise OCR SDK

asprise.com

docTR

mindee.github.io

Side by side

FigureAsprise OCR SDKdocTR
Price from—Free
Free plan—Yes
Free trialYes—
Open source——
WindowsYesYes
macOSYesYes
LinuxYesYes
API—Yes
Self-hostedYesYes
Plans published31
Spec score · OCR API Software8.4 (#6)8.9 (#2)
Spec score · OCR software7.9 (#27)9.0 (#3)
DeploymentAsprise 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 planNoYes
SupportThe 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

Asprise OCR SDK pricing →

docTR

  • docTR open-source Python libraryFree

docTR pricing →

In their own words

Asprise OCR SDK

Asprise OCR SDK has no write-up on Specifiction yet; its figures are above.

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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.

Visit mindee.github.io