標(biāo)題: Titlebook: Document Analysis and Recognition – ICDAR 2023 Workshops; San José, CA, USA, A Mickael Coustaty,Alicia Fornés Conference proceedings 2023 T [打印本頁] 作者: Insularity 時間: 2025-3-21 17:53
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書目名稱Document Analysis and Recognition – ICDAR 2023 Workshops讀者反饋學(xué)科排名
作者: 旋轉(zhuǎn)一周 時間: 2025-3-21 22:36 作者: 祖?zhèn)?nbsp; 時間: 2025-3-22 02:54
The Adaptability of?a?Transformer-Based OCR Model for?Historical Documentspect for libraries and archives that must digitise various sources. The digitisation process cannot rely solely on manual transcription due to the complexity and diversity of historical materials. Therefore, text recognition models must be able to adapt to various printed texts and manuscripts, espe作者: 證實 時間: 2025-3-22 05:37 作者: hankering 時間: 2025-3-22 10:29
A Survey and?Approach to?Chart Classificationy conveyed numerically. In the scientific literature, there are many charts, each with its stylistic differences. Recently the document understanding community has begun to address the problem of automatic chart understanding, which begins with chart classification. In this paper, we present a surve作者: Gum-Disease 時間: 2025-3-22 13:39 作者: Gum-Disease 時間: 2025-3-22 20:02 作者: penance 時間: 2025-3-22 23:07 作者: Crumple 時間: 2025-3-23 02:43 作者: 共和國 時間: 2025-3-23 09:35
MuraNet: Multi-task Floor Plan Recognition with?Relation Attentions can result in ineffective utilization of relevant information when there are multiple tasks present simultaneously. To address this challenge, we introduce MuraNet, an attention-based multi-task model for segmentation and detection tasks in floor plan data. In MuraNet, we adopt a unified encoder c作者: CHANT 時間: 2025-3-23 13:44 作者: 鍵琴 時間: 2025-3-23 14:18
FPNet: Deep Attention Network for?Automated Floor Plan Analysis to recognize room boundaries and room types in CAD floor plans. We evaluate our network on multiple datasets. We perform quantitative analysis along three metrics - Overall accuracy, Mean accuracy, and Intersection over union (IoU) to evaluate the efficacy of our approach. We compare our approach w作者: 中古 時間: 2025-3-23 18:42 作者: 相反放置 時間: 2025-3-23 23:07 作者: offense 時間: 2025-3-24 02:26
Can Pre-trained Language Models Help in?Understanding Handwritten Symbols?lving a wide array of machine learning tasks in other modalities like images, audio, music, sketches and so on. These language models are domain-agnostic and as a result could be applied to 1-D sequences of any kind. However, the key challenge lies in bridging the modality gap so that they could gen作者: BATE 時間: 2025-3-24 07:50 作者: ALERT 時間: 2025-3-24 11:33
Breathing is to live, to smell and to feeldocuments, even in multilingual environments. These models require minimal training data and are a suitable solution for digitising libraries and archives. However, it is essential to note that the quality of the recognised text can be affected by the handwriting style.作者: Distribution 時間: 2025-3-24 16:03 作者: MAOIS 時間: 2025-3-24 19:23 作者: patella 時間: 2025-3-25 00:29
The Adaptability of?a?Transformer-Based OCR Model for?Historical Documentsdocuments, even in multilingual environments. These models require minimal training data and are a suitable solution for digitising libraries and archives. However, it is essential to note that the quality of the recognised text can be affected by the handwriting style.作者: Nucleate 時間: 2025-3-25 07:09
Segmentation-Free Alignment of?Arbitrary Symbol Transcripts to?Imagesme symbol alignment techniques that are suitable for low-data scenarios and provide an insight on their perceived strengths and weaknesses. In particular, we study the usage of Connectionist Temporal Classification models, Attention-Based Sequence to Sequence models and we compare them with the results obtained on a few-shot recognition system.作者: 冬眠 時間: 2025-3-25 10:20 作者: sinoatrial-node 時間: 2025-3-25 12:04 作者: radiograph 時間: 2025-3-25 18:28
-Biased Estimators in Data Compressiont remain, such as learning to reconstruct the music notation, recognizing multiple voices, or dealing with artifacts such as lyrics. Finally, we suggest some possible directions for future research. We argue that addressing these challenges is crucial to making OMR a more practical and useful tool for musicians, scholars, and librarians alike.作者: 佛刊 時間: 2025-3-25 22:26
https://doi.org/10.1007/978-1-349-13233-1 state-of-the-art models trained in different domains and analyze the difficulties and challenges associated with generalization. Our study provides insights into the development of more robust deep-learning models for processing comics’ characters and improving their generalization to new domains.作者: 作嘔 時間: 2025-3-26 02:25
Optical Music Recognition: Recent Advances, Current Challenges, and?Future Directionst remain, such as learning to reconstruct the music notation, recognizing multiple voices, or dealing with artifacts such as lyrics. Finally, we suggest some possible directions for future research. We argue that addressing these challenges is crucial to making OMR a more practical and useful tool for musicians, scholars, and librarians alike.作者: 內(nèi)閣 時間: 2025-3-26 06:52 作者: eustachian-tube 時間: 2025-3-26 11:56
Can Pre-trained Language Models Help in?Understanding Handwritten Symbols?erate strong features beneficial for out-of-domain tasks. This work focuses on leveraging the power of such pre-trained language models and discusses the challenges in predicting challenging handwritten symbols and alphabets.作者: 先行 時間: 2025-3-26 13:36
0302-9743 ce on Document Analysis and Recognition, ICDAR 2023, held in San José, CA, USA, during August 21–26, 2023...The total of 43 regular papers presented in this book were carefully selected from 60 submissions.?..Part I contains 22 regular papers that stem from the following workshops:..ICDAR 2023 Works作者: optic-nerve 時間: 2025-3-26 17:22 作者: 不可接觸 時間: 2025-3-26 23:02
https://doi.org/10.1007/978-3-030-35914-0es through the panels to highlight different aspects of a character’s emotional states. Consequently, for each face image, pictures corresponding to the character’s body and the whole panel are also included so they can be studied simultaneously.作者: 假 時間: 2025-3-27 02:04 作者: addict 時間: 2025-3-27 08:07 作者: calumniate 時間: 2025-3-27 10:56 作者: 委托 時間: 2025-3-27 14:26 作者: glowing 時間: 2025-3-27 20:08 作者: 表示向前 時間: 2025-3-28 00:48 作者: 單調(diào)女 時間: 2025-3-28 02:17 作者: Valves 時間: 2025-3-28 09:25
Breathing is to live, to smell and to feeln methods. Our experiments indicate that the strongest discriminative features do not come from generation artifacts, letter shapes, or the generative model’s architecture, but instead originate from real-world artifacts in genuine handwriting that are not reproduced by generative methods.作者: prolate 時間: 2025-3-28 13:48
https://doi.org/10.1007/978-4-431-99039-0tokens. Our experiments assess the performance of 4 different Spanish LLMs to classify up to 30 different topics in the data in different configurations. The results shows that LLMs can be of great use to process domain-specific documents, such as those in the domain of public affairs.作者: 存心 時間: 2025-3-28 16:52 作者: 使乳化 時間: 2025-3-28 21:13
Paula Kasares,Ane Ortega,Estibaliz Amorrortuition at ICPR 2022. The data set includes 15 different chart categories, including 22,923 training images and 13,260 test images. We have implemented a vision-based transformer model that produces state-of-the-art results in chart classification.作者: collagen 時間: 2025-3-28 23:57 作者: contradict 時間: 2025-3-29 04:57
Karel van Hulle,Leo van der Tasents..The evaluations were carried out on the KABOOM-ONOMATOPOEIA dataset and show the relevance of our method in comparison with methods of the literature, which makes it a promising tool in the field of scene text detection.作者: 出血 時間: 2025-3-29 09:38 作者: Trochlea 時間: 2025-3-29 15:11
Document Analysis and Recognition – ICDAR 2023 WorkshopsSan José, CA, USA, A作者: 猛烈責(zé)罵 時間: 2025-3-29 15:52
Beyond Human Forgeries: An Investigation into?Detecting Diffusion-Generated Handwritingn methods. Our experiments indicate that the strongest discriminative features do not come from generation artifacts, letter shapes, or the generative model’s architecture, but instead originate from real-world artifacts in genuine handwriting that are not reproduced by generative methods.作者: osteoclasts 時間: 2025-3-29 20:27
Leveraging Large Language Models for?Topic Classification in?the?Domain of?Public Affairstokens. Our experiments assess the performance of 4 different Spanish LLMs to classify up to 30 different topics in the data in different configurations. The results shows that LLMs can be of great use to process domain-specific documents, such as those in the domain of public affairs.作者: jarring 時間: 2025-3-30 00:22
Using GANs for?Domain Adaptive High Resolution Synthetic Document Generationbased) represented a preliminary step towards the generation of realistic document images, it was impeded by its incapacity to produce high-resolution outputs. Our research aims to overcome this restriction, enhancing the DocSynth model’s capacity to generate high-resolution document images. Additio作者: Mercantile 時間: 2025-3-30 06:39
A Survey and?Approach to?Chart Classificationition at ICPR 2022. The data set includes 15 different chart categories, including 22,923 training images and 13,260 test images. We have implemented a vision-based transformer model that produces state-of-the-art results in chart classification.作者: 躺下殘殺 時間: 2025-3-30 10:30 作者: 元音 時間: 2025-3-30 14:14
Detection of?Buried Complex Text. Case of?Onomatopoeia in?Comics Booksents..The evaluations were carried out on the KABOOM-ONOMATOPOEIA dataset and show the relevance of our method in comparison with methods of the literature, which makes it a promising tool in the field of scene text detection.作者: leniency 時間: 2025-3-30 16:36
On Text Localization in?End-to-End OCR-Free Document Understanding Transformer Without Text Localizad to most existing OCR-free models, making it an attractive solution for practitioners in the field. We validate the method through experiments on document parsing benchmarks, and the results demonstrate its effectiveness in generalizing to various camera-captured document images, such as, receipts 作者: Canvas 時間: 2025-3-31 00:11
Document Analysis and Recognition – ICDAR 2023 Workshops978-3-031-41498-5Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: PAD416 時間: 2025-3-31 04:56
https://doi.org/10.1007/978-3-031-41498-5Document Image Analysis and Recognition; Natural Language Processing; Computational Paleography; Digita
