3D Interpolation for OCR Character Recognition
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Solution Overview
Problem
Existing 2D OCR methods face challenges in accurately distinguishing between similar characters, particularly low-quality inputs, leading to reduced confidence in OCR output and increased processing resources required for error correction.
Innovation Solution
The implementation of 3D interpolation, where a 2D character is given depth using an extrusion transformation to generate a 3D model, allowing for comparison with 3D models of candidate characters, and generating multiple 2D images from different angles to improve character recognition confidence.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If 2D OCR methods are used to recognize characters, then the process is simple and fast, but the accuracy is reduced when distinguishing between similar characters
Solution Approach 1:
The patent transforms 2D character images into 3D models by adding a depth dimension through extrusion. This dimensional transition allows the system to capture spatial relationships and structural features that are invisible in 2D projections, thereby improving character recognition accuracy while maintaining computational feasibility through selective 3D processing only for ambiguous cases
2Reliability
If multiple candidate characters are considered to improve recognition accuracy, then the confidence increases, but the processing time and computational resources increase
Solution Approach 1:
The patent applies 3D modeling and multiple candidate evaluation selectively only to character regions where 2D recognition confidence is below a threshold. This localized application of complex processing ensures high reliability for ambiguous characters while avoiding unnecessary computational overhead for clearly recognizable characters, thus balancing accuracy with processing efficiency
3Measurement precision
If 3D models are generated and compared from multiple angles, then the character recognition accuracy improves, but the computational resources and processing power increase
Solution Approach 1:
The patent generates multiple 2D projection images from 3D models only when needed for disambiguation, rather than processing all characters uniformly. This partial application of 3D analysis—exactly when 2D methods fail—achieves the necessary precision for difficult cases while minimizing overall computational energy consumption by avoiding excessive processing of easily recognizable characters
Data Source
AI summary
To perform 3-dimensional interpolation, a 3-dimensional model of an input text character is generated. For example, a 2-dimensional character may be given depth using an extrusion transformation. The 3-dimensional model of the input text character is compared to 3-dimensional models of candidate characters and the results of the 3-dimensional comparisons are used to select the optical character recognition (OCR) output for the input text character. The 3-dimensional comparison may be performed directly on the 3-dimensional models. Alternatively, a set of 2-dimensional images may be generated for each 3-dimensional model and 2-dimensional comparisons performed. By use of the additional information gathered from the comparisons of the 3-dimensional models, the correct OCR output character can be identified with greater confidence. As a result, the quality of the OCR output is improved, improving the functioning of a computer performing OCR tasks and reducing the expenditure of time and processing power in correcting OCR errors.