Font Database Generation Using Neural Network Models
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Solution Overview
Problem
Current font generation methods, particularly deep learning-based schemes like GANs, require large amounts of high-quality data and struggle to reproduce the joined-up writing characteristic of user fonts, resulting in a lack of aesthetics and diversity in generated fonts.
Innovation Solution
A method involving a trained similarity comparison model to determine a candidate font database most similar to user handwriting data, and a basic font database model to adjust and generate a target font database, using techniques like coherent point drift matching and centroid alignment for radical image adjustment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If GAN-based font generation scheme is used, then font generation quality and diversity are improved, but data quantity requirement increases significantly
Solution Approach 1:
The patent uses a pre-trained basic font database model to generate synthetic training data that copies the statistical characteristics and stylistic features of real handwriting data. This synthetic data is then used to train the GAN model, reducing the need for large quantities of real user handwriting samples while maintaining generation quality and diversity.
Solution Approach 2:
The patent performs preliminary action by pre-training a basic font database model on diverse handwriting data before using it to generate training data for the GAN. This pre-processing step creates a foundation that encapsulates various writing styles and characteristics, which are then transferred to the target user's font generation without requiring extensive user-specific training data.
2Quantity of substance
If decomposition and recomposition font generation scheme is used, then data quantity requirement is reduced, but joined-up writing characteristic and aesthetics are lost
Solution Approach 1:
The patent incorporates feedback mechanisms where the generated font data is continuously evaluated and refined. The system uses the pre-trained basic font database model to provide feedback on generated characters, adjusting the generation process to better capture joined-up writing characteristics and aesthetic qualities while maintaining efficient data utilization.
Solution Approach 2:
The patent creates composite font generation by combining multiple data sources and modeling approaches. It integrates the pre-trained basic font database with user-specific handwriting samples, blending the strengths of both to achieve both data efficiency and high-quality joined-up writing characteristics in the final generated fonts.
3Manufacturing precision
If user-specific font generation is performed, then font accuracy and personalization are improved, but training time and computational cost increase
Solution Approach 1:
The patent develops a universal pre-trained basic font database model that can serve multiple users and purposes. This multi-functional model captures general handwriting characteristics and can be efficiently adapted to individual users through fine-tuning or data augmentation techniques, significantly reducing training time compared to training separate models for each user from scratch.
Solution Approach 2:
The patent utilizes parameter changes by adjusting the pre-trained model's parameters and weights to adapt to different users' handwriting styles. Instead of retraining the entire model for each user, the system modifies specific parameters and incorporates user-specific data selectively, maintaining font accuracy while minimizing training time and computational resources.
Data Source
AI summary
A method of generating a font database, and a method of training a neural network model are provided, which relate to a field of artificial intelligence, in particular to a computer vision and deep learning technology. The method of generating the font database includes: determining, by using a trained similarity comparison model, a basic font database most similar to handwriting font data of a target user in a plurality of basic font databases as a candidate font database; and adjusting, by using a trained basic font database model for generating the candidate font database, the handwriting font data of the target user, so as to obtain a target font database for the target user.