Ideogram Character Analysis via Stroke Segmentation
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Solution Overview
Problem
Optical character recognition (OCR) systems face challenges in accurately identifying ideogram characters due to factors such as image clarity, background interference, and variations in handwriting or font styles.
Innovation Solution
The method involves partitioning an ideogram character into strokes, mapping each stroke to a unique identifier, and creating a sequence of these identifiers. Candidate ideogram characters with stroke identifier sequences within a threshold distance to the original are selected, and these candidates are used to create new phrases for search purposes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional OCR methods are used to identify ideogram characters, then the process is simple and fast, but the recognition accuracy is low due to image clarity issues, background interference, and font variations
Solution Approach 1:
The patent segments an ideogram character into multiple strokes, where each stroke is identified and mapped to a stroke identifier. This segmentation approach transforms the complex task of recognizing entire ideogram characters into simpler stroke-level recognition, thereby improving accuracy while managing system complexity through modular processing
Solution Approach 2:
The patent introduces stroke identifiers as an intermediary representation between the original ideogram image and the final character recognition result. By mapping strokes to standardized identifiers and creating stroke sequences, the system mediates the recognition process to handle variations in handwriting and printing styles, improving robustness without requiring direct complex image analysis
2Measurement precision
If stroke partitioning and mapping is performed to improve ideogram recognition, then recognition accuracy improves, but processing time and computational complexity increase
Solution Approach 1:
The patent performs preliminary action by pre-defining stroke identifiers and establishing a standardized mapping between stroke types and identifiers before the actual recognition process. This preprocessing of stroke classification rules enables faster real-time recognition by eliminating the need for complex runtime analysis of stroke characteristics
Solution Approach 2:
The patent applies partial action by focusing recognition efforts on key distinguishing strokes rather than analyzing every pixel or detail of the character. By identifying and mapping only the essential strokes that define character identity, the system achieves accurate recognition with reduced computational overhead compared to exhaustive image analysis
3Reliability
If candidate characters with threshold distance are selected, then OCR error correction improves, but the search space and computational load increase
Solution Approach 1:
The patent segments the search space by organizing candidate characters based on their stroke sequences. Instead of comparing against all possible ideogram characters, the system narrows the search to candidates with matching or similar stroke patterns, significantly reducing the search space while maintaining comprehensive error correction capability
Solution Approach 2:
The patent applies a threshold distance criterion to perform partial searching, where only candidate characters within a specified stroke sequence distance are considered. This partial action approach balances thoroughness with efficiency by examining enough candidates to correct typical OCR errors without exhaustively searching the entire character set, thus managing computational load
Data Source
AI summary
Ideogram character analysis includes partitioning an original ideogram character into strokes and mapping each stroke to a corresponding stroke identifier (id) to create an original stroke id sequence that includes stroke identifiers. A candidate ideogram character that has a candidate stroke id sequence within a threshold distance to the original stroke id sequence is selected. One or more embodiments may create a new phrase by replacing the original ideogram character with the candidate ideogram character in a search phrase. One or more embodiments perform a search using the search phrase and the new phrase to obtain a result and present the result. One or more embodiments may replace an original ideogram character in a character recognized document with the candidate ideogram character and store the character recognized document.