HOG Feature-Based OCR for Industrial Character Recognition
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Solution Overview
Problem
Existing optical character recognition (OCR) systems in industrial environments face challenges with low contrast, noise, character variation, and skew, requiring complex parameter adjustments and expert intervention, making them difficult to set up and maintain, especially for non-expert users.
Innovation Solution
The implementation of a Histogram of Oriented Gradients (HOG) feature-based OCR system that uses a sliding window approach, eliminating the need for character segmentation and requiring fewer parameters to adjust, allowing for robust and computationally efficient character recognition without the need for segmentation sub-processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional OCR segmentation approach is used, then character recognition can be performed, but the system requires complex parameter adjustments and expert intervention, making it difficult to set up and maintain
Solution Approach 1:
The patent applies segmentation by dividing the image into multiple bins or regions, and processing each bin independently to generate HOG features. This segmentation approach simplifies the overall recognition task by breaking down complex images into manageable units, each contributing to the final character identification without requiring complex global parameter adjustments
Solution Approach 2:
The patent changes the fundamental parameters of the OCR approach by using HOG features instead of traditional segmentation parameters. By transforming the problem into feature space and using correlation-based matching, the system eliminates the need for manual adjustment of numerous segmentation parameters, reducing setup complexity while maintaining recognition accuracy
2Measurement precision
If traditional OCR segmentation approach is used, then character recognition can be performed, but it requires tens of parameters that might require adjustment, requiring significant training and experience
Solution Approach 1:
The system performs self-service by automatically computing HOG features from the input image and self-adjusting through correlation-based matching with character models. The algorithm independently determines optimal bin configurations and feature extractions without requiring user intervention or expert knowledge, making the system easy to operate while maintaining high accuracy
Solution Approach 2:
The patent replaces the mechanical parameter-adjustment system with an automated computational system. Instead of manually tuning tens of segmentation parameters, the system uses algorithmic feature extraction and correlation matching, substituting manual mechanical adjustment with automated computational processes that require no specialized training
3Device complexity
If HOG feature-based approach is used, then parameter adjustments are reduced, but the system must process and compare multiple character models at each image location
Solution Approach 1:
The patent extracts only the essential HOG features from the image and from character models, comparing only these extracted features rather than processing entire images. This extraction approach reduces computational power requirements by focusing only on the discriminative gradient information needed for character recognition, eliminating redundant processing
Data Source
AI summary
Systems, methods, and articles to provide robust optical character recognition (OCR) for use in industrial environments. One or more implementations include utilizing Histogram of Oriented Gradients (HOG) features with a sliding window approach as a robust and computationally efficient method of OCR. The implementations are relatively simple to use because there are relatively few parameters to adjust, which allows a non-expert user to setup or modify the system and achieve desirable performance. One reason this is possible is because the implementations described herein do not require character segmentation, which can be difficult to optimize.