Automated Graphical Data Extraction via CNN and OCR
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Solution Overview
Problem
Current tools for data extraction from graphical representations lack an end-to-end automated solution, requiring manual selection of graphical attributes and axis calibration, leading to time-consuming, error-prone processes with non-uniform data outputs that are often unhelpful for direct comparison across different types of graphical representations.
Innovation Solution
An automated system using convolutional neural networks to analyze graphical representations, convert them to a standard format, detect features, classify chart types, and extract data using optical character recognition, storing the data in a centralized database for uniform analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual selection of graphical attributes and axis calibration is used, then data extraction can be performed, but the process becomes time-consuming and error-prone
Solution Approach 1:
The system performs self-calibration by automatically detecting axis boundaries, scales, and data points without requiring manual intervention. The automated feature detection and classification algorithms enable the system to extract data independently, eliminating time-consuming manual operations while maintaining extraction accuracy through sophisticated image processing and machine learning techniques.
Solution Approach 2:
Manual mechanical processes of selecting graphical attributes and calibrating axes are replaced with automated computational systems. Convolutional neural networks and optical character recognition algorithms substitute human operators, automatically identifying chart elements, interpreting scales, and extracting data points, thereby reducing both time consumption and human error.
2Adaptability or versatility
If conventional data extraction tools are used, then some data can be extracted, but the output data is not uniform and lacks consistency across different graphical representations
Solution Approach 1:
The system is designed with universal functionality to handle multiple types of graphical representations including bar charts, line graphs, pie charts, and scatter plots. The automated classification algorithm identifies different chart types and applies appropriate extraction methods for each, ensuring consistent data uniformity across diverse graphical formats while maintaining adaptability to various visualization styles.
Solution Approach 2:
The system dynamically adjusts extraction parameters based on the detected chart type and graphical attributes. By automatically identifying scale ranges, axis labels, and data point distributions, the system modifies its extraction methodology to maintain data uniformity across different graphical representations, ensuring consistent output formats regardless of the input chart variety.
3Reliability
If manual review and calibration are performed, then data quality can be maintained, but the process requires significant human effort and is inefficient
Solution Approach 1:
The system performs self-validation through automated quality checks, including verification of detected features, validation of extracted data ranges, and consistency checks across multiple data points. This self-service quality assurance mechanism maintains high data reliability while eliminating the need for manual review, thereby significantly improving extraction efficiency and productivity.
Solution Approach 2:
The system implements feedback loops where extracted data is validated against expected patterns and ranges. The automated quality control mechanisms provide real-time feedback on extraction confidence levels, allowing the system to adjust its processing parameters and re-extract data if quality thresholds are not met, thereby maintaining reliability without requiring manual intervention.
Data Source
AI summary
Embodiments of the invention are directed to systems, methods, and computer program products for a unique platform for analyzing, classifying, extracting, and processing information from graphical representations. Embodiments of the inventions are configured to provide an end to end automated solution for extracting data from graphical representations and creating a centralized database for providing graphical attributes, image skeletons, and other metadata information integrated with a graphical representation classification training layer. The invention is designed to receive a graphical representation for analysis, intelligently identify and extract objects and data in the graphical representation, and store the data attributes of the graphical representation in an accessible format in an automated fashion.