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Amazon SageMaker is a fully-managed platform that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale. With Amazon SageMaker, all the barriers and complexity that typically slow down developers who want to use machine learning are removed. The service includes models that can be used together or independently to build, train, and deploy your machine learning models.

Mphasis DeepInsights Table Detection

By: Mphasis Latest Version: 2.3

Mphasis DeepInsights Table Detection helps in detecting and locating the tabulated information from scanned images.

Product 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 : 1. jpeg/jpg 2. png

Key Data

Version
By Mphasis
Categories
Type Model Package
Fulfillment Methods Amazon SageMaker

Usage Information

Fulfillment Methods

Amazon SageMaker

Instructions:

  1. Download the IPython Notebook from the link below.
  2. Upload the notebook onto a SageMaker Notebook Instance OR Install necessary packages on the desired compute resource to use the notebook.
  3. Bring in the input images for Table Detection onto the SageMaker Notebook Instance OR on the desired compute resource.
  4. Image file size<4 mb. We are supporting .png and .jpg image formats .
  5. 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.
  6. 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.json

Resources

Support Information

Mphasis DeepInsights Table Detection

For any assistance reach out to us at:

https://www2.mphasis.com/AWS-Marketplace-Support-LP.html

AWS Infrastructure

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Refund Policy

Currently we do not support refunds, but you can cancel your subscription to the service at any time.

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