Sold by: Mphasis
Deployed on AWS
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Computer vision based solution to correct contrast/ brightness in scanned documents to improve performance of document processing pipelines.
Overview
Scanned documents sometimes can have pages with low contrast and low brightness. This can create challenges while processing of documents viz. OCR, ICR, Text extraction, image-based ML/AI modelling, etc. This solution incorporates statistical models which identify the contrast between background and text in the pages of the scanned document and adjust the contrast of the low contrast pages. It then enhances the brightness and text visibility if it finds text to have lost thickness.This enables OCR / ICR engines to achieve higher accuracy and improves the subsequent text extraction pipelines.
Highlights
- This solution can be used to correct contrast and brightness issues which occur while scanning a document with scanner or phone or images captured in low light.
- To correct the contrast and brightness of the pages, this solution performs operations on each pixel of the image and runs statistical models to identify the variation in pixel colors. These variations are fed to equations to calculate the correct adjustment of contrast for each pixel.
- 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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Dimension | Description | Cost/host/hour |
|---|---|---|
ml.m5.xlarge Inference (Batch) Recommended | Model inference on the ml.m5.xlarge instance type, batch mode | $8.00 |
ml.m5.xlarge Inference (Real-Time) Recommended | Model inference on the ml.m5.xlarge 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 |
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Delivery details
Amazon SageMaker model
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Version release notes
Bug Fixes and Performance Improvement
Additional details
Inputs
- Summary
Following are the mandatory inputs for contrast correction algorithm on scanned documents:
- Supported content type: application/zip
- The algorithm detects and corrects low contrast in scanned documents.
- The algorithm works with scanned documents in formats – PDFs and Images. The input documents must be zipped.
- Images can be of following types - bmp, dib, jpeg, jpg, jpe, png, pbm, pgm, ppm, tiff, tif
- Limitations for input type
- The input zip file can have up to 10 images. A scanned document in PDF format can have maximum 10 pages.
- Input MIME type
- application/zip
https://github.com/Mphasis-ML-Marketplace/Refined-Contrast-using-Computer-Vision/tree/main/Input
https://github.com/Mphasis-ML-Marketplace/Refined-Contrast-using-Computer-Vision/tree/main/Input
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