Sold by: MphasisÂ
Deployed on AWS
Comprehensive solution which uses Deep learning & Computer Vision to correct tilt, contrast/brightness & flipped pages in scanned documents.
Overview
Scanned documents can have pages with wrong alignment, low contrast, low brightness and rotated upside down. This can create challenges while processing of documents using OCR, ICR, Text extraction, image-based ML/AI modelling, etc. This solution incorporates statistical models which identify angle of tilt based on textual orientation and position of text relative to page boundaries and corrects the alignment / tilt of the pages. It identifies the contrast between background and text in the pages of the scanned document and adjust the contrast of the low contrast pages. It also incorporates deep learning models which identify if a page is upside down. The models are trained on a large dataset of thousands of pages. This enables OCR/ICR engines to achieve higher accuracy and improves the subsequent text extraction pipelines.
Highlights
- This solution can be used to correct alignment issues, contrast and brightness issues, tilted and upside-down pages which occur while scanning a document with scanner or phone. This solution can be applied in business scenarios such as customer onboarding, insurance broker submissions, financial statement analysis, KYC, Contract analysis, Entity extraction, etc.
- This solution incorporates statistical and CNN models trained on a large dataset of documents which can identify image quality issues with a document and correct those issues. It can also be used for image augmentation.
- 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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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 |
Vendor refund policy
Currently we do not support refunds, but you can cancel your subscription to the service at any time.
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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 and Performance Improvement
Additional details
Inputs
- Summary
- The algorithm works with scanned documents in formats – PDFs and Images.
- The input documents can be zipped, pdf or images
- The input zip file can have multiple images or PDFs. It must not have a mix of image and pdf
- Also the image size must be less than 2 MB and PDF size must be less than 5 MB
- Zip file must be less than 100 MB
- Images can be of following types - bmp, dib, jpeg, jpg, jpe, png, pbm, pgm, ppm, tiff, tif
- Input MIME type
- application/zip, application/pdf, image/*
https://github.com/Mphasis-ML-Marketplace/Comprehensive-Scanned-Doc-Pre-processing-v3/tree/main/Input
https://github.com/Mphasis-ML-Marketplace/Comprehensive-Scanned-Doc-Pre-processing-v3/tree/main/Input
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