Sold by: Upstage
Deployed on AWS
Extract tables and figures from any document
Overview
Upstage Document Parse is a powerful API designed to automatically convert any document to HTML. It detects layout elements such as paragraphs, tables, images, equations, charts and more to determine the structure of the document. The API then serializes the elements according to reading order, and finally converts the document into HTML.
Highlights
- ### Key Features **Text Recognition** detects text via OCR or PDF parsing, excelling in English and CJK documents, including digital-born PDFs. **Layout Element Detection (LED)** identifies paragraphs, figures, tables, and captions, arranging them in human reading order - great for complex layouts. **Table Structure Recognition (TSR)** converts complex tables to HTML, handling merged cells and hidden gridlines.
- ### Key Applications The Upstage Document Parse model enhances LLM-based document processing and information retrieval by preserving contextual information better than traditional OCR. It is valuable for scenarios where LLMs process documents, integrating RAG with Layout Analysis via embedding techniques. It excels in information extraction and recognizing document structures across various templates, making it ideal for handling the same type of documents in different formats.
- ### Key Tasks - Document OCR - Document Parsing - Layout Analysis - Information Extraction - Layout Element Detection - Table Structure Recognition
Details
Sold by
Categories
Delivery method
Latest version
Deployed on AWS
New
Introducing multi-product solutions
You can now purchase comprehensive solutions tailored to use cases and industries.
Features and programs
Financing for AWS Marketplace purchases
AWS Marketplace now accepts line of credit payments through the PNC Vendor Finance program. This program is available to select AWS customers in the US, excluding NV, NC, ND, TN, & VT.
Pricing
Pricing is based on actual usage, with charges varying according to how much you consume. Subscriptions have no end date and may be canceled any time.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Description | Cost/host/hour |
|---|---|---|
ml.g6.2xlarge Inference (Real-Time) Recommended | Model inference on the ml.g6.2xlarge instance type, real-time mode | $10.00 |
ml.g5.2xlarge Inference (Batch) Recommended | Model inference on the ml.g5.2xlarge instance type, batch mode | $10.00 |
ml.m5.12xlarge Inference (Batch) | Model inference on the ml.m5.12xlarge instance type, batch mode | $10.00 |
ml.p3.2xlarge Inference (Real-Time) | Model inference on the ml.p3.2xlarge instance type, real-time mode | $10.00 |
ml.g5.xlarge Inference (Real-Time) | Model inference on the ml.g5.xlarge instance type, real-time mode | $10.00 |
ml.g5.2xlarge Inference (Real-Time) | Model inference on the ml.g5.2xlarge instance type, real-time mode | $10.00 |
ml.g4dn.xlarge Inference (Real-Time) | Model inference on the ml.g4dn.xlarge instance type, real-time mode | $10.00 |
ml.g6.xlarge Inference (Real-Time) | Model inference on the ml.g6.xlarge instance type, real-time mode | $10.00 |
ml.g5.xlarge Inference (Batch) | Model inference on the ml.g5.xlarge instance type, batch mode | $10.00 |
ml.g6.xlarge Inference (Batch) | Model inference on the ml.g6.xlarge instance type, batch mode | $10.00 |
Dimensions summary
You pay by the hour for the compute instance that runs Document Parse, billed per host hour. Pick from CPU and GPU instance types across the m5, p3, g4dn, g5, and g6 families. Each instance comes in two processing modes: real-time, for immediate responses, and batch, for processing groups of documents. Larger instances (higher xlarge sizes) offer more capacity and cost more per hour. Your total depends on which instance you choose, the mode, and how many hours you run it. There is no upfront commitment.
Top-of-mind questions for buyers
Real-time mode keeps the instance running to return parsed results immediately, one request at a time. Batch mode processes groups of documents together, which suits large jobs run on a schedule. Both bill per host hour. Choose real-time for interactive workflows and batch for bulk document sets.
Charges apply per host hour while the instance runs. A stopped instance does not accrue software charges. Real-time instances keep running until you stop them, so they meter continuously. Batch instances meter only while a job runs. Underlying AWS storage fees may still apply to stopped resources.
One host hour is one hour that your chosen instance runs Document Parse. You pay for running time, not per page or per document. Processing more documents within the same hour does not add charges. Your bill depends on the instance type, the mode, and the hours you run.
console.upstage.ai+1
Helpful?
Vendor refund policy
We do not support any refunds currently.
Legal
Vendor terms and conditions
Upon subscribing to this product, you must acknowledge and agree to the terms and conditions outlined in the vendor's End User License Agreement (EULA) .
Content disclaimer
Vendors are responsible for their product descriptions and other product content. AWS does not warrant that vendors' product descriptions or other product content are accurate, complete, reliable, current, or error-free.
Delivery details
Amazon SageMaker model
An Amazon SageMaker model package is a pre-trained machine learning model ready to use without additional training. Use the model package to create a model on Amazon SageMaker for real-time inference or batch processing. Amazon SageMaker is a fully managed platform for building, training, and deploying machine learning models at scale.
Deploy the model on Amazon SageMaker AI using the following options:
Version release notes
🚀 Updates
- General stability improvements.
Additional details
Inputs
- Summary
Provide input data in multipart form data. View more detailed description here - https://console.upstage.ai/api/document-digitization/document-parsing#request-body
- Input MIME type
- multipart/form-data, application/pdf, image/jpeg, image/png, image/tiff, image/bmp, application/octet-stream, application/vnd.openxmlformats-officedocument.wordprocessingml.document, application/vnd.openxmlformats-officedocument.presentationml.presentation, application/vnd.openxmlformats-officedocument.spreadsheetml.sheet, application/vnd.hancom.hwpx, application/x-hwp
https://console.upstage.ai/_next/image?url=%2Fassets%2Fimages%2Fdocs%2Finvoice.png&w=1920&q=75
S3 path is required.
s3://test-assets/document-parse/input/test_image.jpg
Resources
Vendor resources
Support
Vendor support
Contact us for model inquiries.
AWS infrastructure support
AWS Support is a one-on-one, fast-response support channel that is staffed 24x7x365 with experienced and technical support engineers. The service helps customers of all sizes and technical abilities to successfully utilize the products and features provided by Amazon Web Services.
Similar products
Parse PDFs, images, and scans into LLM-ready HTML. VLM-powered analysis for complex tables & charts.
YomiToku is a proprietary document analysis engine specialized for Japanese. It integrates AI OCR plus layout and table parsing models, accurately structuring vertical text, multi column documents, and complex business forms. It supports a wide range of use cases, including generating data for RAG / search, creating searchable PDFs, and extracting information from table data.
We provide yomitoku-client as a client SDK to help you use this product more conveniently.For more details, please refer to the link below:
https://github.com/MLism-Inc/yomitoku-client
For long-term or large-scale use, this product is also available through private offers.Please contact our support team for pricing information.
Extract structured data from any document, regardless of type or format, using semantic understanding and user-defined schemas.
A first-of-a-kind enrichment model, capable of transforming unstructured documents into rich, highly structured knowledge graphs.
Customer reviews
No customer reviews yet
Be the first to review this product . We've partnered with PeerSpot to gather customer feedback. You can share your experience by writing or recording a review, or scheduling a call with a PeerSpot analyst.