Sold by: MphasisÂ
Deployed on AWS
This solution extracts all tabular data pertaining to the Profit & Loss Statements in a company's annual report.
Overview
This solution takes an annual report in a digital PDF format as input, and returns the JSON data corresponding to the extracted tabular data from the report's Profit & Loss statements. The solution utilizes computer vision techniques to identify the pages with tables, as well as text-based classification techniques to determine the relevance of the pages in question. This is done extremely quickly, rendering it unnecessary for an individual to go through hundreds of pages before finding the required information.
Highlights
- The Profit & Loss Data Extractor uses text classification techniques to identify the pages containing information about Profit & Loss. It then utilizes table detection and extraction algorithms to narrow the search to only those pages that contain tabular data, before finally retrieving it.
- This solution can be used in industries like consulting, banking, financial services, insurance, retail, healthcare, pharmaceuticals, manufacturing, airlines, etc to automate processes like financial spreading, vendor/merchant risk assessment, fundamental analysis etc.
- DeepInsights is a cloud-based cognitive computing platform that offers data extraction & predictive analytics capabilities. Need customized Image Analytics 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.m5.2xlarge Inference (Batch) Recommended | Model inference on the ml.m5.2xlarge instance type, batch mode | $20.00 |
ml.m5.2xlarge Inference (Real-Time) Recommended | Model inference on the ml.m5.2xlarge instance type, real-time mode | $10.00 |
ml.m4.4xlarge Inference (Batch) | Model inference on the ml.m4.4xlarge instance type, batch mode | $20.00 |
ml.m5.4xlarge Inference (Batch) | Model inference on the ml.m5.4xlarge instance type, batch mode | $20.00 |
ml.m4.16xlarge Inference (Batch) | Model inference on the ml.m4.16xlarge instance type, batch mode | $20.00 |
ml.p3.16xlarge Inference (Batch) | Model inference on the ml.p3.16xlarge instance type, batch mode | $20.00 |
ml.m4.2xlarge Inference (Batch) | Model inference on the ml.m4.2xlarge instance type, batch mode | $20.00 |
ml.c5.2xlarge Inference (Batch) | Model inference on the ml.c5.2xlarge instance type, batch mode | $20.00 |
ml.p3.2xlarge Inference (Batch) | Model inference on the ml.p3.2xlarge instance type, batch mode | $20.00 |
ml.c4.2xlarge Inference (Batch) | Model inference on the ml.c4.2xlarge instance type, batch mode | $20.00 |
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
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Content disclaimer
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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 enhancements
Additional details
Inputs
- Summary
Usage Methodology for the algorithm:
- The input must be 'Input.zip' file.
- The zip file should contain Input file which has a .pdf file.
- The PDF file should be a non-encrypted digital file with content not as scanned images.
- Name of the folder inside the zip file should be “Input” which is case-sensitive
- check the instructions and sample endpoint in the sample jupyter file provided.
Input file structure:- Input.zip |--Input |--sample_financial_report.zip
- Limitations for input type
- only one (annual report) pdf in a zip file
- Input MIME type
- application/zip
https://github.com/Mphasis-ML-Marketplace/Profit-Loss-Statement-Extractor/blob/main/Input/Input.zip
https://github.com/Mphasis-ML-Marketplace/Profit-Loss-Statement-Extractor/blob/main/Input/Input.zip
Resources
Vendor resources
Support
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