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

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书目名称Proceedings of the Future Technologies Conference (FTC) 2024, Volume 3影响因子(影响力)学科排名




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书目名称Proceedings of the Future Technologies Conference (FTC) 2024, Volume 3网络公开度学科排名




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书目名称Proceedings of the Future Technologies Conference (FTC) 2024, Volume 3年度引用学科排名




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书目名称Proceedings of the Future Technologies Conference (FTC) 2024, Volume 3读者反馈学科排名

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

积极词汇

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

羽毛长成

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

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