Sold by: John Snow Labs
Deployed on AWS
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Transform complex documents into structured, schema-compliant JSON using the top-ranked self-hosted OCR model. Built for enterprise document automation, JSL Vision OCR Structured LLM extracts data from PDFs, forms, tables, and scanned documents while keeping sensitive information within your AWS environment.
Overview
Vision OCR Structured LLM is an enterprise-grade vision-language model designed to convert complex documents into structured, application-ready data.
Unlike traditional OCR solutions that stop at text extraction, JSL Vision OCR Structured LLM understands document structure and produces schema-compliant JSON outputs that can be consumed directly by business applications, analytics platforms, RAG systems, and automation workflows.
Organizations can eliminate manual document processing, reduce custom parsing logic, and accelerate document-driven workflows by extracting structured information directly from PDFs, forms, reports, tables, and scanned documents.
The model is optimized for production environments where reliability matters. Through schema-aware decoding, it generates guaranteed-valid JSON outputs, eliminating malformed responses, post-processing pipelines, and costly validation workflows.
Key business benefits include:
- Reduce manual document review and data entry
- Automate extraction from forms, reports, invoices, and business documents
- Convert unstructured content into structured JSON for downstream systems
- Accelerate document ingestion, analytics, and workflow automation
- Improve consistency and reliability of extracted data
- Deploy securely within your AWS environment while maintaining full control of sensitive information
- Support compliance and governance requirements through self-hosted deployment
The model is particularly well suited for Intelligent Document Processing (IDP), document automation, financial workflows, healthcare documentation, regulatory reporting, claims processing, enterprise search, and AI-ready data preparation.
Designed for organizations that require both performance and operational control, the model delivers industry-leading structured document extraction while running efficiently on a single GPU, making advanced document intelligence accessible without complex infrastructure requirements.
Performance
- 0.714 JSON-Diff accuracy on OmniOCR - #1 OS, #5 overall in the JSL Vision Benchmark Series
- Superior performance on Schema constrained OCR: Claude Sonnet 4.5 (0.709), Holo2-30B-A3B (0.684), Qwen3-VL-8B (0.676), Pixtral-Large (0.670)
- 0.268 CER on FUNSD flat-text OCR (100 pages)
IMPORTANT USAGE INFORMATION:
After subscribing to this product and creating a SageMaker endpoint, billing occurs on an HOURLY BASIS for as long as the endpoint is running.
- Charges apply even if the endpoint is idle and not actively processing requests
- To stop charges, you MUST DELETE the endpoint in your SageMaker console
- Simply stopping requests will NOT stop billing.
This ensures you are only billed for the time you actively use the service.
Highlights
- Industry-Leading Performance: >>Runs efficiently on a single NVIDIA A10G (24 GB) GPU. >>32K context length for multi-page documents >>Supports PDF, PNG, JPG, and any image-convertible format
- Structured Extraction Excellence: >>Superior JSON generation from complex document layouts >>Excellent chart and data visualization comprehension >>Advanced table extraction with structure preservation >>Robust handling of nested tables and hierarchical data >>Reliable key-value extraction from challenging layouts >>Generates guaranteed-valid JSON outputs through schema-aware decoding. >>Eliminates post-processing, JSON repair, and schema-validation workflows.
- >>Converts PDFs, forms, reports, tables, and scanned documents into application-ready data. >>Keeps sensitive data within your AWS environment with no dependency on external AI APIs. >>Delivers deterministic, consistent outputs suitable for regulated and compliance-sensitive workflows. >>Optimized for healthcare, financial services, insurance, legal, government, and enterprise document processing.
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Pricing
Free trial
Try this product free for 15 days according to the free trial terms set by the vendor.
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.g5.2xlarge Inference (Batch) Recommended | Model inference on the ml.g5.2xlarge instance type, batch mode | $9.98 |
ml.g5.2xlarge Inference (Real-Time) Recommended | Model inference on the ml.g5.2xlarge instance type, real-time mode | $9.98 |
ml.g5.12xlarge Inference (Batch) | Model inference on the ml.g5.12xlarge instance type, batch mode | $9.98 |
ml.g5.12xlarge Inference (Real-Time) | Model inference on the ml.g5.12xlarge instance type, real-time mode | $9.98 |
ml.g5.xlarge Inference (Batch) | Model inference on the ml.g5.xlarge instance type, batch mode | $9.98 |
ml.g5.4xlarge Inference (Batch) | Model inference on the ml.g5.4xlarge instance type, batch mode | $9.98 |
ml.g5.8xlarge Inference (Batch) | Model inference on the ml.g5.8xlarge instance type, batch mode | $9.98 |
ml.g5.xlarge Inference (Real-Time) | Model inference on the ml.g5.xlarge instance type, real-time mode | $9.98 |
ml.g5.4xlarge Inference (Real-Time) | Model inference on the ml.g5.4xlarge instance type, real-time mode | $9.98 |
ml.g5.8xlarge Inference (Real-Time) | Model inference on the ml.g5.8xlarge instance type, real-time mode | $9.98 |
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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
Model Optimization
Additional details
Inputs
- Summary
1. Chat Completion
Example Payload {
"model": "/opt/ml/model",
"messages": [
{"role": "system", "content": "You are a helpful medical assistant."},
{"role": "user", "content": "What should I do if I have a fever and body aches?"}
],
"max_tokens": 1024,
"temperature": 0.6
}For additional parameters:
ChatCompletionRequest OpenAI Chat API
2. Text Completion
Single Prompt Example {
"model": "/opt/ml/model",
"prompt": "How can I maintain good kidney health?",
"max_tokens": 512,
"temperature": 0.6
}
Multiple Prompts Example {
"model": "/opt/ml/model",
"prompt": [
"How can I maintain good kidney health?",
"What are the best practices for kidney care?"
],
"max_tokens": 512,
"temperature": 0.6
}
Reference:3. Image + Text Inference
The model supports both online (direct URL) and offline (base64-encoded) image inputs.
Online Image Example { "model": "/opt/ml/model", "messages": [ {"role": "system", "content": "You are a helpful medical assistant."}, { "role": "user", "content": [ {"type": "text", "text": "What does this medical image show?"}, {"type": "image_url", "image_url": {"url": "https://example.com/image.jpg "}} ] } ], "max_tokens": 2048, "temperature": 0.1 } Offline Image Example (Base64) { "model": "/opt/ml/model", "messages": [ {"role": "system", "content": "You are a helpful medical assistant."}, { "role": "user", "content": [ {"type": "text", "text": "What does this medical image show?"}, {"type": "image_url", "image_url": {"url": "data:image/jpeg;base64,..."}} ] } ], "max_tokens": 2048, "temperature": 0.1 } Reference:
4. Structured Output (JSON Schema)
Force the model to output valid JSON matching a specific schema using response_format.
Example with Schema
{ "model": "/opt/ml/model", "messages": [ { "role": "system", "content": "Extract patient information as JSON." }, { "role": "user", "content": "Patient John Doe, age 45, has hypertension." } ], "temperature": 0.0, "max_tokens": 512, "response_format": { "type": "json_schema", "json_schema": { "name": "patient_info", "strict": true, "schema": { "type": "object", "required": ["name", "age", "conditions"], "properties": { "name": {"type": "string"}, "age": {"type": "integer"}, "conditions": { "type": "array", "items": {"type": "string"} } } } } } }
Reference:
Important Notes:
- Streaming Responses: Add "stream": true to your request payload to enable streaming
- Model Path Requirement: Always set "model": "/opt/ml/model" (SageMaker's fixed model location)
- Input MIME type
- application/json
https://github.com/JohnSnowLabs/spark-nlp-workshop/tree/master/products/sagemaker/models/jsl_vision_ocr_structured_light/inputs
https://github.com/JohnSnowLabs/spark-nlp-workshop/tree/master/products/sagemaker/models/jsl_vision_ocr_structured_light/inputs
Support
Vendor support
For any assistance, please reach out to support@johnsnowlabs.com .
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