Titlebook: Proceedings of the Future Technologies Conference (FTC) 2024, Volume 3; Kohei Arai Conference proceedings 2024 The Editor(s) (if applicabl

召集會議

書目名稱Proceedings of the Future Technologies Conference (FTC) 2024, Volume 3影響因子(影響力)




書目名稱Proceedings of the Future Technologies Conference (FTC) 2024, Volume 3影響因子(影響力)學(xué)科排名




書目名稱Proceedings of the Future Technologies Conference (FTC) 2024, Volume 3網(wǎng)絡(luò)公開度




書目名稱Proceedings of the Future Technologies Conference (FTC) 2024, Volume 3網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Proceedings of the Future Technologies Conference (FTC) 2024, Volume 3被引頻次




書目名稱Proceedings of the Future Technologies Conference (FTC) 2024, Volume 3被引頻次學(xué)科排名




書目名稱Proceedings of the Future Technologies Conference (FTC) 2024, Volume 3年度引用




書目名稱Proceedings of the Future Technologies Conference (FTC) 2024, Volume 3年度引用學(xué)科排名




書目名稱Proceedings of the Future Technologies Conference (FTC) 2024, Volume 3讀者反饋




書目名稱Proceedings of the Future Technologies Conference (FTC) 2024, Volume 3讀者反饋學(xué)科排名

最有利

Salma Alghamdi,Lama Al Khuzayem,Ohoud Al-Zamzamions, composite background, noise etc. and language specific issues like cursive connectivity among the characters etc. makes OCR challenging and erroneous for Indian languages. The language specific challenges can be overcome by computing the script-based features and can achieve better accuracy. Co

Inflamed

Jubilation

Bowen Sun,Hoi-Sim Wong,Shibao Zhengith flat named entities, whereas entities are often nested. For example, a postal address might contain a street name and a number. This work compares three nested NER approaches, including two state-of-the-art approaches using Transformer-based architectures. We introduce a new Transformer-based ap

monologue

Muhammad Arslan,Muhammad Mubeen,Syed Muhammad Usmanays, Masked Image Modeling?(MIM) shows superiority in visual representation learning, and several works introduce it into text recognition. In this paper, we take a further step and design a method for text-recognition-friendly self-supervised feature learning. Specifically, we propose to decouple v

積極詞匯

Vasanth Iyer,Igor Ternovskiye-structure can be recognized with impressive accuracy using Image-to-Markup-Sequence (Im2Seq) approaches. Taking only the image of a table, such models predict a sequence of tokens (e.g. in HTML, LaTeX) which represent the structure of the table. Since the token representation of the table structur

scrutiny

羽毛長成

Verónica de Jesús Pérez Franco,Ana Lilia Coria Páez,Jesús Jaime Moreno Escobar,Oswaldo Morales Matamoros,Erika Yolanda Aguilar del Villar,Mauro Daniel Castillo Pérezpre-training. In contrast, humans can usually identify key-value pairings from a form only by looking at layouts, even if they don’t comprehend the language used. No prior research has been conducted to investigate how helpful layout information alone is for form understanding. Hence, we propose a u

maintenance

Ashutosh Sagar,Ishan Makadia,Meet Sinojia,Zahra Sadeghi,Stan MatwinMost works do not consider the long-tailed distribution issue in oracle character recognition, resulting in a biased DNN towards head classes. To overcome this issue, we propose a two-stage decoupled learning method to train an unbiased DNN model for long-tailed oracle character recognition. In the

全神貫注于