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Deployed on AWS
Mphasis DeepInsights Table Detection helps in detecting and locating the tabulated information from scanned images.
Overview
Table Detection is a component of DeepInsights, which helps in detecting the tabulated data present inside unstructured documents. It is a Deep Learning model which localizes and separates tables from free-text in documents. The end result will contain the highlighted boundaries of the tabular portions as well as the table-detection probability for that highlighted region. This model ingests the files in various image formats and outputs the image containing highlighted table blocks. Supported image formats are :
- jpeg/jpg
- png
Highlights
- Automated information extraction of tables from documents that helps in augmenting manual intervention for such tasks. This saves a lot of time for analysts, insurance brokers, data entry operators and helps in increasing their productivity.
- Model uses state of the art RESNET Deep Learning network to accurately identify and highlight tables in unstructured documents.
- DeepInsights is a cloud-based cognitive computing platform that offers data extraction & predictive analytics capabilities. Need customized Image Analytics solutions? Get in touch!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
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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 |
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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
Instructions:
- Download the IPython Notebook from the link below.
- Upload the notebook onto a SageMaker Notebook Instance OR Install necessary packages on the desired compute resource to use the notebook.
- Bring in the input images for Table Detection onto the SageMaker Notebook Instance OR on the desired compute resource.
- Image file size<4 mb. We are supporting .png and .jpg image formats .
- Following are the Types of tables which will be accurately detected by Model. a. The Table should be fully or partially bounded by the lines on all sides. b. The Columns inside the table must have proper spacing or separating lines between them. c. In case of Multi-Table Image Document, the tables shouldn't be adjacent. d. The Table should contain more than two columns if column-headers are not present. e. Image should have proper separation between free-text and tabular regions for accurate predictions.
- Follow the instructions in the IPython Notebook for rest of the setup and consuming the service.
Input
Supported content types: application/json The images need JSON serialized to be fed to the model. Code can be found in jupyter notebook.
Output
Content type: application/json
Please use the following snippet to save the json content into a image file (details in jupyter notebook ):
def prediction_wrapper(prediction): p_json_parse = json.loads(prediction) return p_json_parse['processedImage'] table_image = np.array(prediction_wrapper(prediction)) cv2.imwrite("image_process.png",table_image)Invoking endpoint
AWS CLI Command
You can invoke endpoint using AWS CLI:
aws sagemaker-runtime invoke-endpoint --endpoint-name "endpoint-name" --body fileb://input.json --content-type application/json --accept application/json out.jsonResources
- Input MIME type
- application/json
See Input Summary
See Input Summary
Resources
Vendor resources
Support
Vendor support
For any assistance reach out to us at:
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