Missing Value Test

Execute Test:

The code snippet provided outlines the process of setting up and executing a Missing Value Test in RagaAI, focusing on detecting data points where text detection and recognition have been missed.

Step 1: Define the Outlier Detection Rules

Begin by establishing the criteria for detecting missing values in your dataset.

rules = OcrRules()
rules.add(expected_detection={"invoice_date": 1,"total_gross_worth": 1})


ocr_test = ocr_missing_test_analysis(test_session=test_session,
                             dataset_name = "Invoices_Test",
                             test_name = "OCR Missing Value Test",
                             model = "dbnet",
                             type = "ocr",
                             output_type="missing_value",
                             rules = rules)

test_session.add(ocr_test)

test_session.run()

• OCRRules(): Initialises the rules for missing value detection for OCR Usecases.

  • rules.add(): Adds a rule for detecting missed values:

    • expected_detection: Sets the expected classes to be present in each datapoint.

• ocr_missing_test_analysis(): Configures the missing value test with the following parameters:

  • test_session: The session object tied to your RagaAI project.

  • dataset_name: The name of the dataset you are analysing, "Invoices_Test" here.

  • test_name: A name for the test, "OCR Missing Value Test" in this case.

  • model: Specifies the OCR model used for inferences, "dbnet" here.

  • type: Specify the usecase, "ocr" here.

  • output_type: For OCR use cases use output_type = "missing value".

  • rules: The previously defined rules for missing value detection.

• test_session.add(): Registers the outlier detection test within the session.

• test_session.run(): Starts the execution of all tests in the session, including the outlier detection test.

By completing these steps, you have initiated a Missing Value Test for OCR application on the RagaAI Testing Platform.

After the test, carefully review the identified missing values and do a root cause analysis to take remedial actions.

Analysing Test Results

Test Overview

• Pie Chart: Provides an overview of the test results using a pie chart compares the percentage of Bbox of classes accurately detected in a dataset according to the expected values set by the user

Failed Cases Per Class

• Bar Graph: This helps users visualise the count of missing values per class.

Assessing and Visualising Data

• Datagrid View: Examine images in the grid view. View datapoints where detections are being missed and drill down to gain more insights.

Interpreting Results

• Expected values: Number of expected bounding boxes of a class in a datapoint. This information is set by the user while executing the test

• Actual: Number of bounding boxes detected of a class in a datapoint.

By adhering to these steps, you can effectively utilise RagaAI to detect and analyse datapoints where detections are being missed in your OCR datasets.