Font Retrieval Neural Network for Tag-Based Search
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Solution Overview
Problem
The proliferation of digital fonts has led to challenges in efficiently retrieving desired fonts due to subjective and non-standardized font tags, resulting in inaccurate and inconsistent results in conventional tag-based font search systems, which are often biased towards popular tags and require significant computational resources.
Innovation Solution
The use of deep learning neural networks, specifically a combination of font tag recognition and tag-based font retrieval models, to generate font affinity scores that provide a comprehensive measure of font relevance to multi-tag queries, addressing biases and improving accuracy and flexibility in font retrieval.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional tag-based font search systems are used, then font retrieval can be performed, but the results are biased towards popular tags and lack accuracy
Solution Approach 1:
The patent introduces neural networks as an intermediary between font tags and font retrieval. The neural network learns objective mappings from heterogeneous font tags to font characteristics, eliminating direct bias from popular tags. This intermediary layer transforms subjective tags into objective affinity scores, resolving the contradiction between accuracy and consistency.
Solution Approach 2:
The patent changes the parameter representation from direct tag matching to learned affinity scores through neural networks. By transforming the retrieval metric from simple tag frequency to complex learned representations, the system achieves both accuracy and consistency in font retrieval results.
2Measurement precision
If simple tag probability combination is used, then computation is efficient, but bias from popular tags persists and comprehensive measurement is lost
Solution Approach 1:
The patent performs preliminary training of neural networks to learn font-tag affinity mappings before actual retrieval. This preliminary action captures complex relationships during training, enabling efficient inference during retrieval without recalculating complex probabilities each time, thus achieving comprehensive measurement with reasonable computational cost.
Solution Approach 2:
The patent uses neural networks to learn and copy the complex relationships between fonts and tags during training. Once learned, these relationships are stored as model parameters, allowing efficient retrieval without repeatedly performing complex computations, thus balancing comprehensive measurement with computational efficiency.
3Adaptability or versatility
If subjective and non-standardized font tags are used, then font variety and customization are enhanced, but retrieval accuracy and consistency deteriorate
Solution Approach 1:
The patent creates a universal neural network model that handles diverse and heterogeneous font tags through a unified learning framework. The model learns to interpret various tag formats and meanings consistently, enabling both tag diversity and retrieval accuracy to coexist by providing a universal interpretation layer.
Solution Approach 2:
The patent transforms heterogeneous tag parameters into a unified affinity score space through neural network learning. By changing the parameter representation from diverse tag formats to standardized learned embeddings, the system maintains tag diversity while achieving consistent and accurate retrieval measurements.
Data Source
AI summary
The present disclosure describes a font retrieval system that utilizes a multi-learning framework to develop and improve tag-based font recognition using deep learning neural networks. In particular, the font retrieval system jointly utilizes a combined recognition/retrieval model to generate font affinity scores corresponding to a list of font tags. Further, based on the font affinity scores, the font retrieval system identifies one or more fonts to recommend in response to the list of font tags such that the one or more provided fonts fairly reflect each of the font tags. Indeed, the font retrieval system utilizes a trained font retrieval neural network to efficiently and accurately identify and retrieve fonts in response to a text font tag query.