Completion Logic for OCR of Ambiguous 3D Alphanumeric Sequences
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Solution Overview
Problem
Conventional Optical Character Recognition (OCR) techniques fail to accurately identify alphanumeric sequences due to issues like bad lighting, oblique image perspectives, and poor image quality, leading to incomplete or inaccurate renditions and reduced user trust.
Innovation Solution
Leverage completion logic by using machine-learning models trained on reflection pattern data, perspective data, and predetermined syntax to predict unknown or uncertain values in alphanumeric sequences, combining multiple modalities such as context information and additional imaging techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional Optical Character Recognition (OCR) techniques are used to identify alphanumeric sequences, then the process is simple and fast, but the accuracy deteriorates under poor lighting, oblique perspectives, or low image quality
Solution Approach 1:
The patent combines multiple recognition modalities including conventional OCR, completion logic based on predetermined syntax, and machine-learning models into a unified recognition system. This integration allows the system to leverage strengths of each approach, achieving high accuracy in challenging conditions without requiring a complete replacement of simple OCR methods.
Solution Approach 2:
The patent introduces completion logic and machine-learning models as intermediary components that bridge the gap between simple OCR and accurate recognition. These intermediaries process OCR output, identify indeterminate characters, and provide predicted values based on contextual syntax and learned patterns, thereby improving accuracy without directly modifying the core OCR engine.
2Measurement precision
If multiple recognition modalities and machine-learning models are integrated to improve accuracy, then character recognition accuracy improves, but the system complexity increases
Solution Approach 1:
The patent segments the recognition system into distinct functional modules: OCR processing, completion logic based on predetermined syntax, and machine-learning-based prediction. Each module operates independently on specific aspects of the recognition task, allowing for targeted optimization and easier maintenance while achieving comprehensive accuracy improvements through their coordinated output.
3Measurement precision
If completion logic and machine-learning models are used to predict indeterminate characters, then recognition accuracy improves, but the processing time increases
Solution Approach 1:
The patent applies partial action by selectively invoking completion logic and machine-learning models only for indeterminate characters identified by OCR, rather than processing the entire sequence through all modalities. This approach achieves accuracy improvements where needed while minimizing unnecessary processing time for already confidently recognized characters.
Data Source
AI summary
Techniques for leveraging completion logic may include: obtaining imaging data from a user device; detecting, via optical character recognition (OCR), an indeterminate alphanumeric sequence in the imaging data, the indeterminate alphanumeric sequence having at least one three-dimensional alphanumeric character with an indeterminate value; obtaining reflective pattern data of the alphanumeric sequence; predicting at least one value for the at least one three-dimensional alphanumeric character based on the reflective pattern data; and determining a complete alphanumeric sequence based on the at least one predicted value of the at least one three-dimensional alphanumeric character.