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- 저자 : Ammar Ul Hassan (검색결과 6 건)
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FreeType 엔진에 새로운 폰트 서비스를 추가하기 위한 컴포넌트 모듈 설계 및 구현
아마르울하산 ( Ammar Ul Hassan ),김성민 ( Sungmin Kim ),최재영 ( Jaeyoung Choi ) 한국정보처리학회 2017 한국정보처리학회 학술대회논문집 Vol.24 No.1
FreeType is a rasterizer which is commonly used in different operating systems like Linux, android etc. Although FreeType is open source but it is not easy to add/remove modules and services etc. for developers. This paper proposes a new module for FreeType named as FreeType Outlet adapter (FOA). It enables to add/ remove modules, services, functionality etc. inside FreeType. It acts as the bridge to add functions from outside FreeType to the inner core of FreeType. New font formats like METAFONT, animated fonts and customized fonts which currently are not supported by FreeType can be added with this FOA module.
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Few-Shot Image Synthesis using Noise-Based Deep Conditional Generative Adversarial Nets
Finlyson Mwadambo Msiska,Ammar Ul Hassan,최재영(Jaeyoung Choi),유재원(Jaewon Yoo) 한국스마트미디어학회 2021 스마트미디어저널 Vol.10 No.1
In recent years research on automatic font generation with machine learning mainly focus on using transformation-based methods, in comparison, generative model-based methods of font generation have received less attention. Transformation-based methods learn a mapping of the transformations from an existing input to a target. This makes them ambiguous because in some cases a single input reference may correspond to multiple possible outputs. In this work, we focus on font generation using the generative model-based methods which learn the buildup of the characters from noise-to-image. We propose a novel way to train a conditional generative deep neural model so that we can achieve font style control on the generated font images. Our research demonstrates how to generate new font images conditioned on both character class labels and character style labels when using the generative model-based methods. We achieve this by introducing a modified generator network which is given inputs noise, character class, and style, which help us to calculate losses separately for the character class labels and character style labels. We show that adding the character style vector on top of the character class vector separately gives the model rich information about the font and enables us to explicitly specify not only the character class but also the character style that we want the model to generate.
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SkelGAN: A Font Image Skeletonization Method
( Debbie Honghee Ko ),( Ammar Ul Hassan ),( Saima Majeed ),( Jaeyoung Choi ) 한국정보처리학회 2021 Journal of information processing systems Vol.17 No.1
In this research, we study the problem of font image skeletonization using an end-to-end deep adversarial network, in contrast with the state-of-the-art methods that use mathematical algorithms. Several studies have been concerned with skeletonization, but a few have utilized deep learning. Further, no study has considered generative models based on deep neural networks for font character skeletonization, which are more delicate than natural objects. In this work, we take a step closer to producing realistic synthesized skeletons of font characters. We consider using an end-to-end deep adversarial network, SkelGAN, for font-image skeletonization, in contrast with the state-of-the-art methods that use mathematical algorithms. The proposed skeleton generator is proved superior to all well-known mathematical skeletonization methods in terms of character structure, including delicate strokes, serifs, and even special styles. Experimental results also demonstrate the dominance of our method against the state-of-the-art supervised image-to-image translation method in font character skeletonization task.
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CKFont2: 한글 구성요소를 이용한 개선된 퓨샷 한글 폰트 생성 모델
박장경 ( Jangkyoung Park ),( Ammar Ul Hassan ),최재영 ( Jaeyoung Choi ) 한국정보처리학회 2022 정보처리학회논문지. 소프트웨어 및 데이터 공학 Vol.11 No.12
A lot of research has been carried out on the Hangeul generation model using deep learning, and recently, research is being carried out how to minimize the number of characters input to generate one set of Hangul (Few-Shot Learning). In this paper, we propose a CKFont2 model using only 14 letters by analyzing and improving the CKFont (hereafter CKFont1) model using 28 letters. The CKFont2 model improves the performance of the CKFont1 model as a model that generates all Hangul using only 14 characters including 24 components (14 consonants and 10 vowels), where the CKFont1 model generates all Hangul by extracting 51 Hangul components from 28 characters. It uses the minimum number of characters for currently known models. From the basic consonants/vowels of Hangul, 27 components such as 5 double consonants, 11/11 compound consonants/vowels respectively are learned by deep learning and generated, and the generated 27 components are combined with 24 basic consonants/vowels. All Hangul characters are automatically generated from the combined 51 components. The superiority of the performance was verified by comparative analysis with results of the zi2zi, CKFont1, and MX-Font model. It is an efficient and effective model that has a simple structure and saves time and resources, and can be extended to Chinese, Thai, and Japanese.
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딥러닝 기반의 한글 폰트 연구를 위한 한글 폰트 데이터셋
고홍희 ( Debbie Honghee Ko ),이현수 ( Hyunsoo Lee ),석정재 ( Jungjae Suk ),( Ammar Ul Hassan ),최재영 ( Jaeyoung Choi ) 한국정보처리학회 2021 정보처리학회논문지. 소프트웨어 및 데이터 공학 Vol.10 No.2
최근 딥러닝에 대한 관심이 증가하면서 이를 이용한 다양한 분야에서 연구가 진행되고 있다. 그러나 딥러닝 기반의 생성 모델을 이용하는 폰트의 자동 생성 연구들은 로마자 및 한자와 같은 몇 언어들에 국한되어 연구되고 있다. 한글 폰트 디자인은 매우 큰 시간과 비용이 들어가는 작업으로, 딥러닝을 이용하면 손쉽게 생성할 수 있다. 한글 폰트를 생성하는 연구는 딥러닝 기반의 생성 모델들과 발맞추기 위해 프로세스 자동화 관점에서 한글 폰트 데이터셋을 준비하는 것이 중요하다. 이를 위하여 본 논문에서는 딥러닝 기반의 한글 폰트 연구를 위한 한글 폰트 데이터셋을 제안하고. 그데이터셋을 구성하는 방법을 기술한다. 본 논문에서 제안하는 한글 폰트 데이터셋을 기반으로 딥러닝 한글 폰트 생성 어플리케이션에 적용하는 과정을 통해 제안하는 데이터셋 구성의 유용성을 보인다. Recently, as interest in deep learning has increased, many researches in various fields using deep learning techniques have been conducted. Studies on automatic generation of fonts using deep learning-based generation models are limited to several languages such as Roman or Chinese characters. Generating Korean font is a very time-consuming and expensive task, and can be easily created using deep learning. For research on generating Korean fonts, it is important to prepare a Korean font dataset from the viewpoint of process automation in order to keep pace with deep learning-based generation models. In this paper, we propose a Korean font dataset for deep learning-based Korean font research and describe a method of constructing the dataset. Based on the Korean font data set proposed in this paper, we show the usefulness of the proposed dataset configuration through the process of applying it to a deep learning Korean font generation application.
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