Trainable OCR Library Auto-Update via Barcode Validation
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Solution Overview
Problem
Conventional optical character recognition (OCR) systems face inefficiencies due to the need for manual training of character libraries, leading to errors and process inefficiencies, especially in applications like chain of custody operations where accuracy is critical.
Innovation Solution
A method and system that enhance trainable OCR performance by automatically identifying and adding unrecognized characters to the OCR library using barcode data, with the OCR algorithm interpreting character strings, validating characters through confidence thresholds, and updating the library accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual training of OCR character libraries is used, then the OCR system can be implemented, but the process is computationally intensive and time-consuming
Solution Approach 1:
The OCR system automatically trains itself by capturing images of documents, extracting character images through OCR processing, and adding unrecognized characters to the font library without manual intervention. This self-service mechanism eliminates the need for manual character library training while continuously improving recognition accuracy.
Solution Approach 2:
The system performs preliminary OCR processing on captured images to extract character images before formally adding them to the font library. This preliminary action prepares the character data in advance, enabling faster and more accurate recognition in subsequent operations without requiring manual preprocessing.
2Reliability
If manual character library training is performed, then the OCR system can recognize characters, but the process requires significant computational resources and reduces efficiency
Solution Approach 1:
The system automatically captures images, processes them through OCR to extract character images, and adds unrecognized characters to the font library without manual intervention. This self-service approach eliminates the computational overhead of manual training while continuously improving recognition accuracy, thereby maintaining high productivity.
Solution Approach 2:
The patent replaces the manual mechanical process of character library training with an automated digital process. The system uses image capture and OCR algorithms to automatically extract and add characters to the library, substituting manual operations with computational processes that are faster and more efficient.
3Ease of operation
If the OCR library is initialized with insufficient character definitions, then the system can start operating, but it frequently fails to identify unrecognized characters resulting in errors
Solution Approach 1:
The font library is designed to be dynamic rather than static. The system automatically captures images, processes them through OCR, extracts character images, and adds unrecognized characters to the library in real-time. This dynamic updating ensures the library continuously adapts to new characters while maintaining system operability from the start.
Solution Approach 2:
The system implements a feedback mechanism where unrecognized characters captured during normal operation are automatically processed through OCR, extracted as character images, and added to the font library. This feedback loop continuously improves character identification accuracy while allowing the system to start operating immediately with minimal initial configuration.
Data Source
AI summary
Systems and methods for enhancing trainable optical character recognition (OCR) performance are disclosed herein. An example method includes receiving, at an application executing on a user computing device communicatively coupled to a machine vision camera, an image captured by the machine vision camera, the image including an indicia encoding a payload and a character string. The example method also includes identifying the indicia and the character string; decoding the indicia to determine the payload; and applying an optical character recognition (OCR) algorithm to the image to interpret the character string and identify an unrecognized character within the character string. The example method also includes comparing the payload to the character string to validate the unrecognized character as corresponding to a known character included within the payload; and responsive to validating the unrecognized character, adding the unrecognized character to a font library referenced by the OCR algorithm.