標題: Titlebook: Proceedings of the Future Technologies Conference (FTC) 2024, Volume 3; Kohei Arai Conference proceedings 2024 The Editor(s) (if applicabl [打印本頁] 作者: 召集會議 時間: 2025-3-21 17:29
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)學科排名
書目名稱Proceedings of the Future Technologies Conference (FTC) 2024, Volume 3網(wǎng)絡公開度
書目名稱Proceedings of the Future Technologies Conference (FTC) 2024, Volume 3網(wǎng)絡公開度學科排名
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讀者反饋學科排名
作者: 最有利 時間: 2025-3-21 20:33
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作者: Inflamed 時間: 2025-3-22 02:35 作者: Jubilation 時間: 2025-3-22 06:50
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作者: monologue 時間: 2025-3-22 11:54
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作者: 積極詞匯 時間: 2025-3-22 16:33
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作者: scrutiny 時間: 2025-3-22 19:08 作者: 羽毛長成 時間: 2025-3-22 22:57
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作者: maintenance 時間: 2025-3-23 04:24
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 作者: 全神貫注于 時間: 2025-3-23 07:14 作者: 尖叫 時間: 2025-3-23 13:08 作者: 發(fā)誓放棄 時間: 2025-3-23 14:23 作者: ironic 時間: 2025-3-23 18:43
Tobias Dorrn,Almuth Müller, both two-stage cascade and one-stage end-to-end architectures, suffer from different issues. The cascade models can benefit from the large-scale optical character recognition (OCR) and MT datasets but the two-stage architecture is redundant. The end-to-end models are efficient but suffer from trai作者: Adrenaline 時間: 2025-3-24 01:12 作者: 語源學 時間: 2025-3-24 06:00 作者: 歡樂東方 時間: 2025-3-24 10:22
Xiaoting Huang,Xuelian Xi,Siqi Wang,Zahra Sadeghi,Asif Samir,Stan MatwinHowever, this work is limited by currently available poetry corpora, which are restricted to few languages and consist mainly of works by well-known classic poets. In this paper, we develop a new large-scale poetry collection, EEBO-verse (Code and dataset is available on .), by automatically identif作者: Androgen 時間: 2025-3-24 14:27 作者: conscribe 時間: 2025-3-24 18:04 作者: 巨頭 時間: 2025-3-24 19:58 作者: 指數(shù) 時間: 2025-3-24 23:21 作者: 字形刻痕 時間: 2025-3-25 07:07
Salma Alghamdi,Lama Al Khuzayem,Ohoud Al-Zamzamisis with varied levels of noise confirms the promising results of character recognition accuracy of the proposed OCR model which out-performs the state-of-the-art OCR systems for Indian scripts. The proposed model achieves 76.70% with test documents consists of 50% noise and 99.98% with test documen作者: NIL 時間: 2025-3-25 08:00
Razan Al-Hamed,Rawan Al-Hamed,Aya Karam,Fatima Al-Qattan,Fatmah Al-Nnaimy,Soraia Oueidand reach a performance similar to the base approach on flat entities. Even though all 3 approaches perform well in terms of F1-scores, joint labelling is most suitable for hierarchically structured data. Finally, our experiments reveal the superiority of the IO tagging format on such data.作者: CAB 時間: 2025-3-25 12:39 作者: Blazon 時間: 2025-3-25 16:31 作者: Cursory 時間: 2025-3-25 20:15
Vasanth Iyer,Igor Ternovskiycy improves significantly, inference time is halved compared to HTML-based models, and the predicted table structures are always syntactically correct. This in turn eliminates most post-processing needs. Popular table structure data-sets will be published in OTSL format to the community.作者: Mortar 時間: 2025-3-26 00:35 作者: anaphylaxis 時間: 2025-3-26 05:50 作者: neutralize 時間: 2025-3-26 11:36 作者: JOG 時間: 2025-3-26 14:59
Cencheng Shendictive at word image level compared to classical static embedding methods. Furthermore, our recognition-free approach with pre-trained semantic information outperforms recognition-free as well as recognition-based approaches from the literature on several Named Entity Recognition benchmark datasets作者: Glutinous 時間: 2025-3-26 18:22 作者: canonical 時間: 2025-3-26 22:57
Yuxin Du,Jing Fan,Ari Happonen,Dassan Paulraj,Micheal Tuapend approaches that directly adopt OCR features as the input of an information extraction module, we propose to use contrastive learning to narrow the semantic gap caused by the difference between the tasks of OCR and information extraction. We evaluate the existing end-to-end methods for VIE on the 作者: CORD 時間: 2025-3-27 03:18
Tobias Dorrn,Almuth Müllerve experiments show that the proposed method outperforms the existing two-stage cascade models and one-stage end-to-end models with a lighter and faster architecture. Furthermore, the ablation studies verify the generalization of our method, where the proposed modal adapter is effective to bridge va作者: erythema 時間: 2025-3-27 05:40
Wisam Bukaita,Guillermo Garcia de Celis,Manaswi Gurram enhance recognition. Experiments on three datasets prove our method can achieve state-of-the-art recognition performance, and cross-dataset experiments on two datasets verify the generality of our method. Moreover, our method can achieve a breakneck inference speed of 104 FPS with a small backbone 作者: 間諜活動 時間: 2025-3-27 11:13
Yeferson Torres Berru,Santiago Jimenez,Lander Chicaiza,Viviana Espinoza Loayzar proposed approach outperforms several existing state-of-the-art approaches, including complex approaches utilizing generative adversarial networks (GANs) and variational auto-encoders (VAEs), on 7 of the datasets, while achieving comparable performance on the remaining 2 datasets. Our findings sug作者: AMITY 時間: 2025-3-27 14:10
Xiaoting Huang,Xuelian Xi,Siqi Wang,Zahra Sadeghi,Asif Samir,Stan Matwined on general domain document images, by fine-tuning them on an in-domain annotated subset of EEBO. In experiments, we find that an appropriately trained image-only classifier performs as well or better than text-based poetry classifiers on human transcribed text, and far surpasses the performance o作者: STALE 時間: 2025-3-27 19:37
Dorsa Soleymani,Mahsa Mousavi Diva,Lovelyn Uzoma Ozougwu,Riasat Mahbub,Zahra Sadeghi,Asif Samir,Stan Matwine-of-the-art in both datasets, achieving a word recognition rate of . and a 2.41 DTW on IRONOFF and an expression recognition rate of . and a DTW of 13.93 on CROHME 2019. This work constitutes an important milestone toward full offline document conversion to online.
