OCR Evaluation Platform Script for Metric-Based Application Selection
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Solution Overview
Problem
Current methods for evaluating optical character recognition (OCR) applications are inefficient due to limited information usage, requiring significant user input and often providing incomplete performance metrics, which can lead to suboptimal selection of OCR applications for specific operations.
Innovation Solution
A computing platform generates a script to evaluate OCR applications by comparing original resources to modified resources generated by different OCR applications, producing metric scores and weighted scores based on relevant performance metrics, thereby identifying the preferred OCR application for a given operation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If current methods are used to evaluate OCR applications, then user input is required and evaluation is performed, but the evaluation is inefficient and provides limited information
Solution Approach 1:
The system automatically evaluates OCR applications by comparing modified resources generated by different OCR applications against original resources, without requiring significant user input. The evaluation script autonomously generates metric scores and weighted scores, enabling self-service evaluation that improves productivity while reducing time loss.
2Loss of information
If limited metrics are used for evaluation, then evaluation is simpler, but the information provided is insufficient for optimal OCR application selection
Solution Approach 1:
The evaluation system segments performance assessment into multiple distinct metrics (e.g., character-level accuracy, word-level accuracy, sentence-level accuracy) and evaluates each separately. This segmentation allows comprehensive information gathering while maintaining manageable system complexity through modular evaluation components.
Solution Approach 2:
The system transitions from single-dimension evaluation to multi-dimensional evaluation by incorporating multiple metrics at different levels (character, word, sentence). This dimensional expansion enriches the information provided without overwhelming complexity, as each dimension can be evaluated independently using the same script framework.
3Measurement precision
If multiple OCR applications are evaluated manually, then detailed analysis is possible, but the process is time-consuming and resource-intensive
Solution Approach 1:
The patent replaces manual evaluation processes with an automated script-based system. The evaluation script mechanically processes resources and generates comparisons between original and modified resources, substituting human manual analysis with automated computational processes. This increases both measurement precision through consistent scripting and productivity through automation.
Data Source
AI summary
Aspects of the disclosure relate to providing improved optical character recognition (OCR). An OCR evaluation platform may generate a script for evaluating OCR performance. The platform may generate modified resources by executing OCR applications to modify original resources. Based on executing the script, the platform may generate comparative analysis information based on comparing the modified resources to the original resource. The platform may generate metric scores based on the comparative analysis information. The metric scores may be used to generate visual representations of the performance of different OCR applications. The platform may generate weighted scores representing the performance of different OCR applications. The platform may identify a preferred OCR application for performing a particular operation. The platform may store correlations between preferred OCR applications and corresponding operations. The platform my cause execution of preferred OCR applications when performing corresponding operations, based on the stored correlations.