Fonts are one of the most basic and core design concepts. Numerous use cases can benefit from an in depth understanding of Fonts such as Text Customization which can change text in an image while maintaining the Font attributes like style, color, size. Currently, Text recognition solutions can group recognized text based on line breaks or paragraph breaks, if the Font attributes are known multiple text blocks can be combined based on context in a meaningful manner. In this paper, we propose two engines: Font Detection Engine, which identifies the font style, color and size attributes of text in an image and a Font Prediction Engine, which predicts similar fonts for a query font. Major contributions of this paper are three-fold: First, we developed a novel CNN architecture for identifying font style of text in images. Second, we designed a novel algorithm for predicting similar fonts for a given query font. Third, we have optimized and deployed the entire engine On-Device which ensures privacy and improves latency in real time applications such as instant messaging. We achieve a worst case On-Device inference time of 30ms and a model size of 4.5MB for both the engines.
Beyond Patches: Global-aware Autoregressive Model for Multimodal Few-Shot Font Generation
Wangxuan Institute of Computer Technology, Peking University（北京大学王学轩计算机技术研究所）；School of Electronics Engineering and Computer Science, Peking University（北京大学电子工程与计算机科学学院）
Deep Learning Pipeline for Preprocessing and Segmenting Cardiac Magnetic Resonance of Single Ventricle Patients from an Image Registry
Institute of Health Informatics（健康信息学研究所）；Institute of Cardiovascular Science（心血管科学研究所）；University College London（伦敦大学学院）；Boston Children’s Hospital（波士顿儿童医院）；University of Michigan（密歇根大学）；The Children’s Hospital of Philadelphia（费城儿童医院）；Nationwide Children’s Hospital（全国儿童医院）；Hospital for Sick Children（SickKids医院（多伦多病童医院））；Ann and Robert H Lurie Children's Hospital of Chicago（芝加哥安与罗伯特·H·卢里儿童医院）；Children's Healthcare of Atlanta Inc（亚特兰大儿童医疗保健集团）；Texas Children's Hospital（德克萨斯儿童医院）
InnoText: A Unified Model for Visual Text Generation and Editing
JD.com, Inc.（京东公司）；University of Chinese Academy of Sciences（中国科学院大学）；Chongqing University of Post and Telecommunications（重庆邮电大学）；Beijing University of Technology（北京工业大学）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；City University of Hong Kong（香港城市大学）；Zhejiang University（浙江大学）
Global-Local Dual Perception for MLLMs in High-Resolution Text-Rich Image Translation
School of Computer Science and Technology, East China Normal University（东华大学计算机科学与技术学院）；Labs, Huawei Technologies Co., LTD（华为技术有限公司2012实验室）；School of Automation and Intelligent Sensing, Shanghai Jiao Tong University（上海交通大学自动化与智能感知学院）
LogicOCR: Do Your Large Multimodal Models Excel at Logical Reasoning on Text-Rich Images?
School of Computer Science, National Engineering Research Center for Multimedia Software, Institute of Artificial Intelligence, and Hubei Key Laboratory of Multimedia and Network Communication Engineering, Wuhan University（计算机学院、多媒体软件国家工程研究中心、人工智能研究院、多媒体与网络通信工程湖北省重点实验室、武汉大学）
