Multi-Mode Input Identification Method for Text Correction
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Solution Overview
Problem
Existing input modes such as handwriting, keyboard, and voice input operate independently with low accuracy and inefficiency, and the recommendation systems fail to support multiple input modes, leading to limited correction and suggestion capabilities.
Innovation Solution
A multi-type input identification method that captures original data from various input devices, converts it into structural units, performs text integration, weight evaluation, and reconstruction to generate candidate content, and selects a unique candidate text for output, enhancing user input efficiency and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple input modes (handwriting, keyboard, voice) are used independently, then each mode can be processed separately, but the accuracy rate and input efficiency remain low
Solution Approach 1:
The patent merges multiple independent input modes (handwriting recognition, voice recognition, keyboard input) into a unified processing system. The system captures original data from any input device, converts it to structure units, and performs integrated text integration and weight evaluation. This combination allows the system to leverage strengths of different input modes while correcting their individual weaknesses, thereby improving both accuracy and efficiency simultaneously.
Solution Approach 2:
The patent creates a universal processing framework that handles multiple types of input data through the same pipeline. The text integration unit and weight evaluation mechanism work uniformly across handwriting, voice, and keyboard inputs, enabling the system to adapt to different input modes without requiring separate processing systems, thus improving overall input efficiency while maintaining high accuracy across all modes.
2Reliability
If the recommendation system runs on single input mode frequency and dictionary data, then the system structure remains simple, but it cannot effectively correct errors or provide suggestions across different input modes
Solution Approach 1:
The patent segments the text processing into distinct functional units: input parsing unit that converts original data to structure units, text integration unit that performs deconstruction and weight evaluation, and filtering and feedback unit that selects candidate texts. This segmentation allows each unit to specialize in specific tasks while working together to achieve comprehensive correction capability across multiple input modes without creating excessive overall system complexity.
Solution Approach 2:
The patent introduces structure units as an intermediary representation between different input modes and the text integration process. By converting handwriting, voice, and keyboard inputs into a common structure unit format, the system enables unified processing and weight evaluation across different input types, enhancing correction capability while maintaining manageable system complexity through standardized intermediate representation.
3Adaptability or versatility
If the system processes each input mode separately with basic sentence corrections, then the processing logic remains simple, but it cannot provide comprehensive recommendations or effective error correction across input modes
Solution Approach 1:
The patent implements dynamic weight evaluation where the importance of different text reference elements is adjusted based on the input mode and contextual analysis. The filtering and feedback unit dynamically selects candidate texts based on weighted evaluation results, allowing the system to adapt its processing focus according to the specific input mode being used while maintaining a unified processing framework, thus achieving multi-mode versatility without proportional increase in processing complexity.
Data Source
AI summary
The disclosure provides an identification method with multi-type input, which is suitable for multiple type input devices. The identification method includes: capturing a corresponding original data through the input devices, and converting the original data into a plurality of structure units correspondingly. Performing a text integration step, deconstructing a text reference element corresponding to the attributes of the structural units based on the structural units and associated elements thereof, and performing a weight evaluation and reconstruction to generate a candidate content according to the text reference element. Making a decision based on the candidate content, outputting the candidate text as a recommended content when the candidate content includes a unique candidate text, and transmitting it to a corresponding output device. An electronic device using the identification method is also provided.