Convolutional Neural Network Handwriting Processor
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Solution Overview
Problem
Existing systems for assessing language skills, particularly through machine-based evaluations, face challenges in providing objective and scalable assessments, as they struggle to accurately score handwritten inputs in real-time across multiple users, especially in poorly networked environments, and often lack the ability to infer objective scores or adapt to time constraints.
Innovation Solution
The implementation of a content management system that includes processors and memories configured to generate digital representations of handwritten inputs, identify and normalize strokes, segment characters, and apply models to generate word hypotheses, enabling real-time analysis and adaptive assessment across multiple users.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If machine-based assessments are implemented to improve scalability, then the number of users that can be assessed increases, but the accuracy of scoring handwritten inputs deteriorates
Solution Approach 1:
The system segments the handwritten input processing into multiple independent components: stroke identification, stroke normalization, character segmentation, and word hypothesis generation. Each component processes specific aspects of the handwriting independently, allowing parallel processing that maintains accuracy while improving scalability.
Solution Approach 2:
The patent introduces an intermediary processing layer between the handwritten input and the final scoring. This layer includes stroke normalization and character segmentation modules that transform raw handwriting into standardized representations, enabling accurate machine-based scoring without sacrificing precision.
2Speed
If real-time processing is implemented to improve response speed, then assessment results are provided faster, but the complexity of the processing system increases
Solution Approach 1:
The real-time processing system is divided into discrete, modular stages: stroke identification, normalization, character segmentation, and word hypothesis. Each stage processes data independently and passes results to the next stage, enabling efficient real-time processing while keeping individual module complexity manageable.
Solution Approach 2:
The system performs preliminary processing of handwritten strokes immediately upon input, normalizing and segmenting strokes before full analysis is complete. This preliminary action prepares data for subsequent processing steps, enabling faster overall response time without requiring the entire system to be overly complex.
3Adaptability or versatility
If adaptive assessment is implemented to improve personalization, then the assessment adapts to individual user performance, but the computational requirements and system complexity increase
Solution Approach 1:
The assessment system dynamically adjusts based on user performance by continuously analyzing handwriting patterns and adapting the assessment difficulty or focus areas. The system modifies processing parameters and assessment criteria in real-time based on detected user capabilities, providing personalization without requiring overly complex infrastructure.
Data Source
AI summary
An improved machine learning system is provided. For example, a content management server may provide real-time analysis of a user's handwriting to assess the user's knowledge of a language, including using a convolution neural network method. The convolution neural network method may be executed to normalize at least some identified strokes in the user's handwritten user input. Normalization may be performed by translating a window comprising a subset of pixels in a digital representation of the handwritten user input amongst a plurality of pixels in the digital representation.