Sold by: Mphasis
Deployed on AWS
This solution categorizes different documents types like contract documents, broker submissions, invoices, etc.
Overview
Document Classifier is a Natural Language Processing based text classification model which analyzes the document text to identify the document type. It ingests documents in pdf format and gives the document type as a text string. Supported Document Types are:
- Commercial Invoices
- Broker Submission Document
- Insurance Claim Forms
- Contract Document The model works well with above document types and can be extended to classify other documents types as well. It can be applied in various use cases like spam filtering, triaging and document indexing
Highlights
- Document classification helps in indexing of different kinds of documents, which improves the turnaround time for such tasks. The automated identification of the document type saves a lot of time and effort for such repetitive tasks, freeing up the analysts time for other important tasks.
- Natural Language Processing based Text Modeling ensures high accuracy. The model can be scaled to process high volumes of documents.
- Mphasis DeepInsights is a cloud-based cognitive computing platform that offers data extraction & predictive analytics capabilities. Need Customized Deep learning and Machine Learning Solutions? Get in Touch!
Details
Sold by
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.m5.large Inference (Batch) Recommended | Model inference on the ml.m5.large instance type, batch mode | $8.00 |
ml.m5.large Inference (Real-Time) Recommended | Model inference on the ml.m5.large instance type, real-time mode | $4.00 |
ml.m4.4xlarge Inference (Batch) | Model inference on the ml.m4.4xlarge instance type, batch mode | $8.00 |
ml.m5.4xlarge Inference (Batch) | Model inference on the ml.m5.4xlarge instance type, batch mode | $8.00 |
ml.m4.16xlarge Inference (Batch) | Model inference on the ml.m4.16xlarge instance type, batch mode | $8.00 |
ml.m5.2xlarge Inference (Batch) | Model inference on the ml.m5.2xlarge instance type, batch mode | $8.00 |
ml.p3.16xlarge Inference (Batch) | Model inference on the ml.p3.16xlarge instance type, batch mode | $8.00 |
ml.m4.2xlarge Inference (Batch) | Model inference on the ml.m4.2xlarge instance type, batch mode | $8.00 |
ml.c5.2xlarge Inference (Batch) | Model inference on the ml.c5.2xlarge instance type, batch mode | $8.00 |
ml.p3.2xlarge Inference (Batch) | Model inference on the ml.p3.2xlarge instance type, batch mode | $8.00 |
Dimensions summary
You pay by the hour for each host running document classification, billed by usage with no upfront commitment. Pricing splits into two modes: batch inference for processing document sets, and real-time inference for on-demand requests. Within each mode, you choose an AWS instance type. Options span general-purpose, compute-optimized, memory-optimized, and GPU-backed families in sizes from large through 24xlarge. Larger instances and GPU types add processing capacity and carry higher hourly rates. Real-time mode offers more instance choices, including burstable and memory-optimized families not available in batch. Your total cost depends on chosen mode, instance size, and hours run.
Top-of-mind questions for buyers
One HostHrs equals one hour that a single chosen instance runs the classifier. Billing meters actual running hours per host. If you run multiple hosts, each accrues its own hours. Your rate depends on the instance type and mode you select.
Batch mode meters host-hours while you process a set of documents in one run, then stops. Real-time mode meters host-hours while an endpoint stays available for on-demand requests. Batch suits scheduled bulk processing; real-time suits continuous, responsive workloads. Real-time offers more instance families.
The platform extracts and analyzes data from many file types, including images, documents, PDFs, emails, and HTML. It applies machine learning and natural language processing to structured and unstructured content. Pricing does not change by file type; you pay by instance host-hours regardless of document format.
www.mphasis.com
Helpful?
Vendor refund policy
Currently we do not support refunds, but you can cancel your subscription to the service at any time.
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
Bug Fixes and Performance Improvement
Additional details
Inputs
- Summary
Input:
Following are the mandatory inputs for predictions made by the algorithm:
pdffile : This is the path of the pdf file stored in S3.
Supported content types for input: application/pdf
Output
Supported content types: text/plain
Sample Output:
The Predicted Document-Type is Broker Submission Document
Invoking endpoint:
If you are using real time inferencing, please create the endpoint first and then use the following command to invoke it:
aws sagemaker-runtime invoke-endpoint --endpoint-name "endpoint-name" --body fileb://input.pdf --content-type application/pdf --accept text/plain output.out
Substitute the following parameters:
"endpoint-name" - name of the inference endpoint where the model is deployed "input.pdf" - input pdf to do the inference on "application/pdf" - MIME type of the given input file (above) "output.txt" - filename where the inference results are written to.
Resources:
Link to Instructions Notebook: https://tinyurl.com/v7z73yr
Link to Sample Input Pdfs: https://tinyurl.com/y4l3muky
Link to Sample Output: https://tinyurl.com/sk3z8yx
- Input MIME type
- application/pdf
See Input Summary
See Input Summary
Resources
Vendor resources
Support
Vendor support
For any assistance reach out to us at:
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
Mphasis DeepInsights Text Summarizer helps in summarizing text documents.
Deep Learning based solution which predicts positive, negative and neutral sentiments from text.
Machine Learning based solution which extracts important Key phrases/words from a corpus of text.
The solution identifies items bought together frequently in a retail shopping cart.
A Deep Learning approach for address extraction from unstructured documents.
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.