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docTR vs Tungsten OmniPage

docTR

mindee.github.io

Tungsten OmniPage

tungstenautomation.com

Side by side

FiguredocTRTungsten OmniPage
Price fromFree—
Free planYes—
Free trial—Yes
Open source——
WindowsYesYes
macOSYesYes
LinuxYesYes
APIYesYes
Self-hostedYesYes
Plans published12
Spec score · OCR software9.0 (#3)9.2 (#1)
DeploymentThe documentation offers templates for a browser demo and API deployment, and documents running docTR on AWS Lambda.The Capture SDK can be deployed on-premises, in containers or on cloud platforms such as Microsoft Azure and Amazon Web Services.
Free planYesNo
Supported inputsbothboth
IntegrationsThe documentation describes loading and sharing models through the Hugging Face Hub.The Standard edition lists output support for PDF, Microsoft Office, Corel WordPerfect and HTML, and can send converted documents to Amazon Kindle.
Primary platformdesktopdesktop
PurposedocTR is a deep learning OCR library for locating and recognizing text in documents, intended for document automation and research.OmniPage converts paper documents, PDFs and images into editable, searchable and shareable digital files.
SupportThe contribution guide directs users with questions to GitHub Discussions and also mentions a #doctr channel on Slack.The OmniPage product page links users to Tungsten Automation customer support.

Plans

docTR

  • docTR open-source Python libraryFree

docTR pricing →

Tungsten OmniPage

  • OmniPage Ultimate€199, paid once
  • OmniPage Standard€99, paid once

Tungsten OmniPage pricing →

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.

Visit mindee.github.io

Tungsten OmniPage

Tungsten OmniPage converts paper documents, PDFs, and images into editable and searchable digital files. Its recognition capabilities include OCR, ICR, and barcode recognition, alongside PDF compression. OmniPage Ultimate can batch-process files from folders or email and route results into preprogrammed workflows; it recognizes more than 120 languages and works with mobile and desktop scanners, all-in-one devices, and multifunction printers. Standard edition output includes PDF, Microsoft Office, Corel WordPerfect, and HTML, with the option to send converted documents to Amazon Kindle. OmniPage Server supports redaction, password protection, and document encryption. The desktop editions are Windows-only, and Ultimate is not compatible with Mac. For developers, the Capture SDK provides APIs to add OCR and imaging features to applications; it is offered for Windows, Linux, and Mac and can be deployed on-premises, in containers, or on cloud platforms such as Microsoft Azure and Amazon Web Services. OmniPage is paid software with a free trial and no free plan. Individual desktop license customers receive unlimited technical support access during their first year, subject to the stated support scope.

Visit tungstenautomation.com