Sold by: MphasisÂ
Deployed on AWS
Given a scanned document, this deep-learning solution enhances the document quality by removing unwanted elements like dots, lines, smudges.
Overview
Noisy Document Images are a problem across areas like insurance claims, legal documents etc. This happens due to multitude of reasons like sensor defects, environment factors like low light or bright light conditons. These noises can be dots, lines and smudges that add extra unwanted pixel values to raw image pixels. For example, if you take/scan a photo in camera, due to poor lighting conditions, there can be shadows (dark patches) in the original image. Our denoising solution leverages deep learning techniques to achieve high-quality images of such noisy documents. The solution is highly suitable for document preprocessing in many downstream systems like OCR( optical character recognition), contextual visual document QA etc.
Highlights
- This solution can be applied for document analysis in business scenarios such as customer onboarding, insurance broker submissions, financial statement analysis, KYC, Contract analysis, OCR entities extraction, etc.
- This solution can be used to correct noises in pixels which occur while compressing images or scanning a document with scanner or phone. This solution incorporates CNN models, which are trained on a large dataset of documents and can identify image noise issues with a document and correct these issues.
- 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!
Details
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Delivery method
Latest version
Deployed on AWS
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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 |
|---|---|---|
ml.m5.large Inference (Batch) Recommended | Model inference on the ml.m5.large instance type, batch mode | $10.00/host/hour |
ml.m4.4xlarge Inference (Batch) | Model inference on the ml.m4.4xlarge instance type, batch mode | $10.00/host/hour |
ml.m5.4xlarge Inference (Batch) | Model inference on the ml.m5.4xlarge instance type, batch mode | $10.00/host/hour |
ml.m4.16xlarge Inference (Batch) | Model inference on the ml.m4.16xlarge instance type, batch mode | $10.00/host/hour |
ml.m5.2xlarge Inference (Batch) | Model inference on the ml.m5.2xlarge instance type, batch mode | $10.00/host/hour |
ml.p3.16xlarge Inference (Batch) | Model inference on the ml.p3.16xlarge instance type, batch mode | $10.00/host/hour |
ml.m4.2xlarge Inference (Batch) | Model inference on the ml.m4.2xlarge instance type, batch mode | $10.00/host/hour |
ml.c5.2xlarge Inference (Batch) | Model inference on the ml.c5.2xlarge instance type, batch mode | $10.00/host/hour |
ml.p3.2xlarge Inference (Batch) | Model inference on the ml.p3.2xlarge instance type, batch mode | $10.00/host/hour |
ml.c4.2xlarge Inference (Batch) | Model inference on the ml.c4.2xlarge instance type, batch mode | $10.00/host/hour |
Vendor refund policy
Currently we do not support refunds, but you can cancel your subscription to the service at any time.
Legal
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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
Bug fixes
Additional details
Inputs
- Summary
Zip file of images.
- Input MIME type
- application/zip
https://github.com/Mphasis-ML-Marketplace/Image-Denoising-for-Document-Processing/blob/main/input.zip
https://github.com/Mphasis-ML-Marketplace/Image-Denoising-for-Document-Processing/blob/main/input.zip
Resources
Vendor resources
Support
Vendor support
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