Automated Graph Output Validation with OCR and Computer Vision
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Solution Overview
Problem
Conventional test automation frameworks face challenges in validating various types of graph outputs, such as bar graphs, line graphs, pie charts, and histograms, requiring manual intervention due to varying reference data formats and difficulties in identifying minor variations in shape and color, especially in real-time or production graphs, leading to inefficiencies and potential disruptions in the testing process.
Innovation Solution
A system and method utilizing Optical Character Recognition (OCR) and Computer Vision (CV) techniques to automatically extract datasets from graph outputs and compare them with reference datasets based on predefined validation criteria, eliminating the need for manual intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If conventional test automation frameworks are used for graph validation, then the testing process can be automated to some extent, but manual intervention is still required for validating graph outputs due to limited support for various graph types and parameters
Solution Approach 1:
The system enables self-service automation by having the validation framework automatically extract data from graphs using OCR and CV techniques, compare against expected values, and generate validation results without requiring human intervention. The framework autonomously handles various graph types (bar graphs, line graphs, pie charts, histograms, stream graphs) and validates multiple parameters including shape, color, and data values.
Solution Approach 2:
The patent replaces manual mechanical validation processes with automated optical and computational systems. OCR (Optical Character Recognition) and CV (Computer Vision) techniques substitute human visual inspection and manual data extraction, enabling automated validation of graph outputs while maintaining high accuracy in detecting graph properties and comparing them against expected results.
2Measurement precision
If manual validation of graph outputs is performed, then accuracy in identifying graph properties can be achieved, but significant time and effort are required
Solution Approach 1:
The system replaces time-consuming manual validation with automated OCR and CV techniques that can rapidly analyze graph images, extract data points, and validate properties. The automated system maintains measurement precision by using sophisticated image processing algorithms to detect graph elements, read text labels, and compare data against expected values, all within seconds rather than minutes or hours of manual work.
Solution Approach 2:
The validation framework creates digital copies of graph data by extracting information from graph images through OCR and CV techniques. These extracted data copies are then compared against expected values stored in the system, enabling rapid validation without requiring manual re-entry or re-analysis of the original graph data.
3Productivity
If conventional automation frameworks validate real-time graphs, then test execution can proceed, but difficulties arise in identifying minor variations in shape and color
Solution Approach 1:
The system replaces human visual inspection with automated CV techniques specifically designed to detect subtle variations in graph properties. The computer vision algorithms can identify minor changes in shape, color, and positioning that would be difficult for humans to detect consistently, while maintaining rapid processing speeds for real-time validation during test execution.
Solution Approach 2:
The validation framework implements feedback mechanisms by comparing extracted graph data against expected values and providing detailed validation results. The system can identify and report minor variations by highlighting specific differences between actual and expected graph properties, enabling continuous improvement of test validation accuracy while maintaining high execution speed.
4Adaptability or versatility
If various types of graphs are validated using conventional frameworks, then comprehensive testing can be performed, but the frameworks require human intervention due to varying reference data formats
Solution Approach 1:
The validation framework achieves universality by supporting multiple graph types (bar graphs, line graphs, pie charts, histograms, stream graphs) and various reference data formats (Excel, Word, PDF, CSV, image paths) through a single automated system. The framework automatically adapts to different graph types and data formats, applying appropriate validation techniques for each without requiring manual configuration or intervention.
Solution Approach 2:
The system dynamically adjusts validation parameters and techniques based on the detected graph type and reference data format. The OCR and CV techniques are configured with appropriate parameters for different graph types, enabling the automated framework to handle diverse validation scenarios uniformly while maintaining high accuracy and eliminating the need for manual intervention.
Data Source
AI summary
This disclosure relates to a method and system method validating a graph output generated during test case execution. The method includes receiving a graph output associated with a test step of a plurality of test steps within a test case; receiving, contemporaneous to receiving the graph output, a test case document comprising information corresponding to the test step; extracting a first dataset corresponding to the plurality of graph regions of the graph output and a reference dataset based on the information within the test case document; comparing the first dataset with the reference dataset; and validating the graph output based on the comparison and a predefined validation criteria.