Optical Character Recognition Using Binary Tree Opcodes
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Solution Overview
Problem
Traditional character recognition systems face inefficiencies in processing time and adaptability due to the need to identify boundary stops or inflection points in printed characters, and they struggle with recognizing multiple language characters and font types.
Innovation Solution
A method and system that utilize opcodes and direction pointers to create a binary tree, where each node is assigned a unique opcode characterization value, allowing for efficient navigation from a root node to a leaf node based on pre-defined features, reducing the need for extensive decision tree analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional character recognition systems trace boundary stops or inflection points and analyze sequence of features through sequential logic decision tree, then character recognition can be performed, but processing time increases and system complexity increases
Solution Approach 1:
The patent segments the character recognition process into distinct phases: boundary tracing to identify stops/inflection points, feature extraction at these points, and classification through decision tree. This segmentation allows optimization of each phase independently, reducing overall processing time while maintaining accuracy.
Solution Approach 2:
The patent performs preliminary boundary tracing and identification of stops/inflection points before the main classification process. By pre-processing and pre-identifying key features, the system reduces the complexity of the subsequent decision tree analysis, thereby reducing processing time.
2Measurement precision
If traditional character recognition systems use sequential logic decision tree with nodes at every boundary stops or inflection points, then character recognition can be performed, but the number of nodes or tree depth increases
Solution Approach 1:
The patent extracts only the essential features from boundary stops and inflection points that are necessary for character recognition. By selecting and extracting only relevant features rather than analyzing all possible features, the system reduces the number of nodes and depth of the decision tree while maintaining recognition accuracy.
3Measurement precision
If traditional character recognition systems are designed for specific character sets, then recognition accuracy for those characters is high, but adaptability to multiple languages and font types is reduced
Solution Approach 1:
The patent creates a universal boundary tracing and feature extraction algorithm that works across multiple languages and font types. The system identifies stops and inflection points in a language-independent manner, making the recognition system adaptable to various character sets while maintaining high accuracy for each specific language and font combination.
Data Source
AI summary
A method and a system are described for performing optical character recognition on an image including a plurality of printed characters. The method includes defining one or more opcodes and direction pointers associated with the plurality of printed characters of a language and a font type, wherein each of the one or more opcodes has an associated unique opcode characterization value. The method includes creating a binary tree comprising a plurality of nodes, wherein each node of the plurality of nodes is assigned the unique opcode characterization value. The method includes retrieving a set of operations associated with the unique opcode characterization value assigned to each of the plurality of nodes. The method includes navigating the binary tree from a root node to a leaf node based on the set of operations, the first pointer value, and the second pointer value until the leaf node is reached.