OCR Segmentation Parameter Selection System
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Solution Overview
Problem
Setting up optical character recognition (OCR) parameters for machine vision systems is challenging, especially for new users, as it often requires technical expertise to adjust segmentation parameters when default settings fail, and existing systems lack user-friendly methods for troubleshooting and parameter tuning.
Innovation Solution
A computer-implemented method for selecting and automatically generating segmentation parameters for OCR, which includes user-selectable options for indicating correct or incorrect segmentations, allowing for manual generation and display of segmentation results, and automatically adjusting parameters based on statistical properties of good characters, thereby simplifying the setup process and improving user interaction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If default segmentation parameters are used, then setup time is reduced, but segmentation accuracy deteriorates when parameters are incorrect
Solution Approach 1:
The system automatically selects and adjusts segmentation parameters based on statistical properties of character images, enabling the system to self-tune without requiring manual intervention from users. This resolves the contradiction by making the system both quick to set up (using defaults) and accurate (through automatic adjustment).
Solution Approach 2:
The system automatically modifies segmentation parameters based on statistical analysis of character images, dynamically adjusting parameters such as stroke width, character height, and intercharacter gap to achieve accurate segmentation without manual user input.
2Measurement precision
If manual parameter adjustment is enabled, then segmentation accuracy can be improved, but device complexity increases
Solution Approach 1:
The system performs automatic parameter selection and adjustment through statistical analysis, eliminating the need for users to manually configure complex segmentation parameters. This maintains high segmentation accuracy while keeping the interface simple and user-friendly.
Solution Approach 2:
The system introduces an automatic parameter selection mechanism that acts as an intermediary between the user and the complex segmentation parameters, translating user intent into optimized parameter settings without requiring users to understand the underlying complexity.
3Measurement precision
If comprehensive parameter lists are provided, then segmentation accuracy can be improved, but ease of operation deteriorates
Solution Approach 1:
The system automatically selects appropriate segmentation parameters through statistical analysis of character images, eliminating the need for users to navigate through comprehensive parameter lists. This maintains segmentation accuracy while significantly improving ease of operation.
Solution Approach 2:
The system extracts and automatically configures only the necessary segmentation parameters based on statistical properties of the input images, removing the need for users to deal with comprehensive parameter lists while maintaining accurate segmentation performance.
Data Source
AI summary
A computer-implemented method for selecting at least one segmentation parameter for optical character recognition is provided. The method can include receiving an image having a character string that includes one or more characters. The method can also include receiving a character string identifying each of the one or more characters. The method can also include automatically generating at least one segmentation parameter. The method can also include performing segmentation on the image having the character string using the at least one segmentation parameter. The method can also include determining if a resultant segmentation satisfies one or more criteria and if the resultant segmentation satisfies the one or more criteria, selecting the at least one segmentation parameter.