Amazon Sagemaker
Amazon SageMaker is a fully-managed platform that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale. With Amazon SageMaker, all the barriers and complexity that typically slow down developers who want to use machine learning are removed. The service includes models that can be used together or independently to build, train, and deploy your machine learning models.
Mphasis DeepInsights Document Segmenter
By:
Latest Version:
1.0
Analyse the layout of a document page's contents and segment logical blocks into categories like paragraphs, tables, images, headers etc.
Product Overview
Analysing a document requires understanding the structure of the contents on its pages. Documents have sections, headers, captions, images, tables and footers. Each of these can provide valuable metadata when trying to extract information. For example: i) Searching for and identifying images can be done via looking for image captions OR ii) Retrieving an entire section might be more informative than retrieving a sentence/passage. This solution is intended to segment and classify the contents on a page. A state of the art image transformer model is trained to consume each page of a document and output regions of a page with bounding box coordinates and corresponding segment label.
Key Data
Version
By
Type
Model Package
Highlights
The solution can be leveraged as part of intelligent document processing pipeline. The module acts an an AI enrichment layer in the document processsing workflow.
Reference use cases to employ this solution:
- Parsing contract documents to identify document sections and tables. This helps search and retrive within the specified sections and tables.
- Parsing annual reports for statements and commentary.
- Parsing statement of work to index and use as a knowledge base.
Mphasis DeepInsights is a cloud-based cognitive computing platform that offers data extraction & predictive analytics capabilities. Need customized Machine Learning and Deep Learning solutions? Get in touch!
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Pricing Information
Use this tool to estimate the software and infrastructure costs based your configuration choices. Your usage and costs might be different from this estimate. They will be reflected on your monthly AWS billing reports.
Contact us to request contract pricing for this product.
Estimating your costs
Choose your region and launch option to see the pricing details. Then, modify the estimated price by choosing different instance types.
Version
Region
Software Pricing
Model Realtime Inference$5.00/inference
running on any instance
Model Batch Transform$10.00/hr
running on ml.m5.xlarge
Infrastructure PricingWith Amazon SageMaker, you pay only for what you use. Training and inference is billed by the second, with no minimum fees and no upfront commitments. Pricing within Amazon SageMaker is broken down by on-demand ML instances, ML storage, and fees for data processing in notebooks and inference instances.
Learn more about SageMaker pricing
With Amazon SageMaker, you pay only for what you use. Training and inference is billed by the second, with no minimum fees and no upfront commitments. Pricing within Amazon SageMaker is broken down by on-demand ML instances, ML storage, and fees for data processing in notebooks and inference instances.
Learn more about SageMaker pricing
SageMaker Realtime Inference$0.23/host/hr
running on ml.m5.xlarge
SageMaker Batch Transform$0.23/host/hr
running on ml.m5.xlarge
Model Realtime Inference
For model deployment as Real-time endpoint in Amazon SageMaker, the software is priced based on the number of inferences generated by the ML Model per month. Typically, the number of inferences is the same as the number of successful calls to the real-time endpoint. For models that support multiple inputs in a request, sellers have the option to meter the number of inputs processed in a request to count generated inferences.
Additional infrastructure cost, taxes or fees may apply.
Usage Information
Model input and output details
Input
Summary
A single zip file needs to be sent as input which should contain the document page image(s) in .jpg or .png format which you wish to run the segmentation model on. Output naming will be performed based on the names of the images passed as input. For an example, refer to the sample Input.zip.
Input MIME type
application/zipSample input data
Output
Summary
A json with the image name(s) as key and bounding box values in a nested list as value
Output MIME type
application/jsonSample output data
Sample notebook
Additional Resources
End User License Agreement
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Support Information
Mphasis DeepInsights Document Segmenter
For any assistance reach out to us at:
AWS Infrastructure
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