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
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Delivery method
Latest version
Deployed on AWS
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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, which converts documents into structured text. Pricing is organized around two things: the AWS instance type you pick and the inference mode. Real-time mode processes requests as they arrive; batch mode handles grouped workloads. Instance types range from smaller options like ml.g4dn.xlarge and ml.g5.xlarge up to larger ones like ml.g5.48xlarge and ml.g6.48xlarge. Larger instances offer more compute capacity. You are billed for host hours used, so cost scales with how long each instance runs.
Top-of-mind questions for buyers
Both modes bill by host hours on the instance you run. Real-time mode processes documents as requests arrive, so the instance runs while it serves live traffic. Batch mode handles grouped documents together in scheduled jobs. You pick the mode that fits your workload; each accrues charges only while the instance runs.
One host hour is one running hour of the AWS instance type you selected, such as ml.g5.xlarge or ml.g6.48xlarge. You are billed for the time the instance runs, whether or not it processes documents. Stopping the instance ends software charges, though AWS storage fees may still apply.
Larger instances offer more compute capacity, which helps with heavier document volumes and complex layouts like tables and charts. Smaller instances suit lighter workloads. Since you pay per host hour, a right-sized instance controls cost. Test with your expected page volume to find the fit.
www.upstage.ai
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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
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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.
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