Hybrid Font Recognition Neural Network for Intra-Class Variance
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Solution Overview
Problem
Existing font classification systems face challenges in accurately recognizing digital fonts, particularly non-Roman fonts like Japanese fonts, due to intra-class variances and high memory and computational requirements, leading to misclassification and instability, especially on mobile devices.
Innovation Solution
A hybrid font recognition neural network is developed, combining multiple deep learning neural networks with a weight prediction neural network to dynamically weight predictions, improving accuracy by reducing intra-class variance effects and optimizing resource usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If recent font classification systems use machine-learning algorithms to recognize fonts, then font recognition capability is improved, but memory and computational requirements increase significantly
Solution Approach 1:
The patent divides the font recognition task into multiple specialized neural networks, each trained on specific glyph types (e.g., kanji-only, kana-only, Roman alphabet). This segmentation allows each network to be more efficient and accurate for its specific domain, reducing the overall computational burden compared to a single comprehensive network.
Solution Approach 2:
The patent creates a universal font recognition system that can handle multiple glyph types through a combination of specialized networks. The system dynamically selects and combines results from different neural networks based on the input characteristics, providing multi-functional capability without requiring each individual network to handle all glyph types.
2Adaptability or versatility
If font classification systems train on large numbers of glyphs (e.g., Japanese fonts with 50,000+ glyphs), then coverage of glyph types improves, but intra-class variances increase leading to misclassification
Solution Approach 1:
The patent segments the large set of Japanese glyphs into distinct groups (kanji, kana, and other characters), creating separate neural networks for each group. This segmentation reduces intra-class variance within each network by ensuring they only process similar glyph types, thereby improving classification accuracy while maintaining comprehensive glyph coverage.
Solution Approach 2:
The patent introduces an intermediary classification stage that first identifies the glyph type before routing to the appropriate specialized neural network. This intermediary step acts as a mediator that prevents mismatched processing, ensuring that each glyph is analyzed by the most appropriate network and reducing misclassification errors.
3Adaptability or versatility
If recent font classification systems process complex glyph sets (e.g., Japanese fonts), then recognition capability for diverse fonts improves, but system stability decreases
Solution Approach 1:
The patent segments the complex font processing task into multiple stable, specialized components. Each neural network is trained on a specific glyph subset, making it more stable and less prone to errors when processing that particular type. This modular approach improves overall system reliability while maintaining support for diverse font languages.
Solution Approach 2:
The patent implements feedback mechanisms where the system evaluates the confidence and accuracy of predictions from each specialized neural network, then dynamically adjusts the weighting and combination of results. This feedback loop ensures that the most reliable predictions are prioritized, maintaining system stability even when processing complex and diverse glyph sets.
Data Source
AI summary
The present disclosure relates to a font recognition system that employs a multi-task learning framework and training to improve font classification and remove negative side effects caused by intra-class variances of glyph content. For example, in one or more embodiments, the font recognition system trains a hybrid font recognition neural network that includes two or more font recognition neural networks and a weight prediction neural network. The hybrid font recognition neural network determines and generates classification weights based on which font recognition neural network within the hybrid font recognition neural network is best suited to classify the font in an input text image. By employing a hybrid trained font classification neural network, the font recognition system can improve overall font recognition as well as remove the negative side effects from diverse glyph content.