A Prototypical Network-Based Approach for Low-Resource Font Typeface Feature Extraction and Utilization

Kangying Li, Biligsaikhan Batjargal and Akira Maeda
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Kangying Li: Graduate School of Information Science and Engineering, Ritsumeikan University, Shiga 525-8577, Japan
Biligsaikhan Batjargal: Kinugasa Research Organization, Ritsumeikan University, Kyoto 603-8577, Japan
Akira Maeda: College of Information Science and Engineering, Ritsumeikan University, Shiga 525-8577, Japan

Data, 2021, vol. 6, issue 12, 1-20

Abstract: This paper introduces a framework for retrieving low-resource font typeface databases by handwritten input. A new deep learning model structure based on metric learning is proposed to extract the features of a character typeface and predict the category of handwrittten input queries. Rather than using sufficient training data, we aim to utilize ancient character font typefaces with only one sample per category. Our research aims to achieve decent retrieval performances over more than 600 categories of handwritten characters automatically. We consider utilizing generic handcrafted features to train a model to help the voting classifier make the final prediction. The proposed method is implemented on the ‘Shirakawa font oracle bone script’ dataset as an isolated ancient-character-recognition system based on free ordering and connective strokes. We evaluate the proposed model on several standard character and symbol datasets. The experimental results showed that the proposed method provides good performance in extracting the features of symbols or characters’ font images necessary to perform further retrieval tasks. The demo system has been released, and it requires only one sample for each character to predict the user input. The extracted features have a better effect in finding the highest-ranked relevant item in retrieval tasks and can also be utilized in various technical frameworks for ancient character recognition and can be applied to educational application development.

Keywords: feature fusion; metric learning; low-resource data (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2021
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