Sold by: John Snow Labs
Deployed on AWS
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Compact, OCR-specialized vision-language model engineered for state-of-the-art grounded OCR in production document workflows. It is the right model when text recognition AND text location both matter: medical de-identification, form-field extraction, compliance redaction, document anonymization, and any pipeline that needs to act on a specific word at a specific position on a specific page
Overview
The Vision OCR LLM is an enterprise-grade OCR-specialized vision-language model engineered for state-of-the-art grounded OCR in production document workflows. It is the right model when text recognition AND text location both matter: medical de-identification, form-field extraction, compliance redaction, document anonymization, and any pipeline that needs to act on a specific word at a specific position on a specific page
The model emits text along with precise word-level bounding-box coordinates in a single inference pass, with no two-stage detection-then-recognition pipeline to maintain, achieving state-of-the-art results across every major OCR benchmarks.
Unlike traditional OCR solutions that only return text, the model is optimized for reading text and returning precise word-level bounding boxes in a single inference pass.
Key capabilities and Ideal Use Cases
- OCR and document understanding for PDFs, images, forms, and scanned documents
- Medical de-identification (PHI redaction with precise coordinates)
- Form-field extraction (mapping values to specific page regions)
- Compliance auditing (which text was flagged, where on the page)
- Document anonymization (region-level masking and blurring)
- Multilingual document processing, table and formula recognition, handwritten text
In independent benchmark evaluations covering leading OCR and vision-language models, John Snow Labs Vision OCR LLM achieved the highest ranking among self-hosted models and outperformed multiple well-known open-source and commercial alternatives on structured document extraction tasks. The model is specifically designed for organizations that require accurate document intelligence while maintaining security, compliance, and operational control
Performance
- 860 on OCRBench (state-of-the-art for models under 3B parameters)
- 94.10 overall on OmniDocBench with 0.042 text edit distance, 94.73 formula, 91.81 table
- 85.21 on Wild-OmniDocBench (degraded scans with folds and lighting changes)
- 91.03 on DocML multilingual document parsing across 14 non-English non-Chinese languages
- 92.29 cards, 92.53 receipts, 92.87 video subtitles on information extraction
- 0.9574 Table TEDS, 0.9706 Formula CDM (English)
- 0.039 BBox CER on FUNSD - #1 of 15 models in the JSL Vision Benchmark Series
- 4.7x lower CER than Tesseract 5.5, 6.1x lower than EasyOCR on the same FUNSD benchmark
- 100% parse rate - valid bounding-box output produced for every page
Built for organizations that require security, control, and high-quality structured outputs, the Vision OCR LLM enables enterprises to unlock value from document repositories while reducing operational costs and accelerating automation initiatives.
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.
Highlights
- Word-level bounding-box output: text plus (x1, y1, x2, y2) coordinates per word; >>32K context length for multi-page inputs; >>Image resolution up to 8MP / 4K (3840x2160): >> Supports PDF, PNG, JPG, and any image-convertible format
- Reduce manual document review and data entry;>>Accelerate document-driven business processes;>>Automate extraction from forms, reports, invoices, and complex PDFs;>>Simplify integration with enterprise applications and data pipelines;
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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 |
Dimensions summary
You pay by the hour for model inference, based on the AWS instance you run. The listing offers five ml.g5 instance sizes: xlarge, 2xlarge, 4xlarge, 8xlarge, and 12xlarge. Each size comes in two modes: batch, for processing groups of documents, and real-time, for on-demand requests. Larger instances add compute capacity and raise the hourly rate. You choose the size and mode that fit your workload. Billing is metered per host hour, so charges reflect actual runtime. This software runs on your own infrastructure, and no separate document or user limits apply.
Top-of-mind questions for buyers
Both modes meter per host hour on the same ml.g5 instance. Batch mode runs inference on groups of documents, so you spin up the host, process the batch, and stop. Real-time mode keeps the host running to answer on-demand requests, so charges accrue for as long as the endpoint stays active.
Charges accrue per host hour while the instance runs. A stopped host does not generate software charges. In real-time mode, an active endpoint keeps billing even between requests, so shut it down when idle. Batch mode only bills during the processing run.
No document, character, or user limits apply. You run the software on your own AWS infrastructure, and all usage is metered by host hour. You can process any volume and run parallel workloads across multiple hosts, with each host billed for its own runtime.
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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
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:
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/inputs
https://github.com/JohnSnowLabs/spark-nlp-workshop/tree/master/products/sagemaker/models/jsl_vision_ocr/inputs
Support
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For any assistance, please reach out to support@johnsnowlabs.com .
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