標題: 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: LEVEE 時間: 2025-3-21 16:09
書目名稱Document Analysis and Recognition – ICDAR 2023 Workshops影響因子(影響力)
書目名稱Document Analysis and Recognition – ICDAR 2023 Workshops影響因子(影響力)學(xué)科排名
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書目名稱Document Analysis and Recognition – ICDAR 2023 Workshops網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Document Analysis and Recognition – ICDAR 2023 Workshops被引頻次
書目名稱Document Analysis and Recognition – ICDAR 2023 Workshops被引頻次學(xué)科排名
書目名稱Document Analysis and Recognition – ICDAR 2023 Workshops年度引用
書目名稱Document Analysis and Recognition – ICDAR 2023 Workshops年度引用學(xué)科排名
書目名稱Document Analysis and Recognition – ICDAR 2023 Workshops讀者反饋
書目名稱Document Analysis and Recognition – ICDAR 2023 Workshops讀者反饋學(xué)科排名
作者: B-cell 時間: 2025-3-21 20:17
Leveraging Knowledge Graph Embeddings to?Enhance Contextual Representations for?Relation Extractionpresentation. We conducted a series of experiments which revealed promising and very interesting results for our proposed approach. The obtained results demonstrated an outperformance of our method compared to context-based relation extraction models.作者: Aesthete 時間: 2025-3-22 01:06
Extracting Key-Value Pairs in?Business Documentstraction in business documents. Our approach is designed to be adaptable and requires minimal semantic and language-specific knowledge, making it suitable for a wide range of business documents. This flexibility allows our method to be easily applied to real-world scenarios, where documents may vary作者: formula 時間: 2025-3-22 04:36 作者: ENDOW 時間: 2025-3-22 10:07
Subgraph-Induced Extraction Technique for?Information (SETI) from?Administrative Documentsraph level and compare the results with baselines on private as well as public datasets. Our model succeeds in improving recall and precision scores for some classes in our private dataset and produces comparable results for public datasets designed for Form Understanding and Information Extraction.作者: 斗爭 時間: 2025-3-22 14:46 作者: 斗爭 時間: 2025-3-22 17:48 作者: 狂熱語言 時間: 2025-3-22 22:44
A Comparison of Demographic Attributes Detection from Handwriting Based on Traditional and Deep Learct the demographical attributes of writers. In the deep learning method, a Convolutional Neural Network model based on the ResNet architecture with a fully connected layer, followed by a softmax layer is used to provide probability scores to facilitate demographic information detection. To evaluate 作者: 延期 時間: 2025-3-23 02:45 作者: Manifest 時間: 2025-3-23 08:13 作者: 罵人有污點 時間: 2025-3-23 10:40
Document Analysis and Recognition – ICDAR 2023 WorkshopsSan José, CA, USA, A作者: Infraction 時間: 2025-3-23 15:45 作者: Urgency 時間: 2025-3-23 20:47 作者: Inflammation 時間: 2025-3-24 01:12
Hugh Rudnick,Constantin Velásquezpresentation. We conducted a series of experiments which revealed promising and very interesting results for our proposed approach. The obtained results demonstrated an outperformance of our method compared to context-based relation extraction models.作者: BOOM 時間: 2025-3-24 03:34
M. R. Hesamzadeh,J. Rosellon,I. Vogelsangtraction in business documents. Our approach is designed to be adaptable and requires minimal semantic and language-specific knowledge, making it suitable for a wide range of business documents. This flexibility allows our method to be easily applied to real-world scenarios, where documents may vary作者: 不透明性 時間: 2025-3-24 07:46
Hugh Rudnick,Constantin Velásqueztention towards relevant tokens without harming model efficiency. We observe improvements on multi-page business documents on Information Retrieval for a small performance cost on smaller sequences. Relative 2D attention revealed to be effective on dense text for both normal and long-range models.作者: 中子 時間: 2025-3-24 14:23
Macmillan Motor Vehicle Engineering Seriesraph level and compare the results with baselines on private as well as public datasets. Our model succeeds in improving recall and precision scores for some classes in our private dataset and produces comparable results for public datasets designed for Form Understanding and Information Extraction.作者: CORE 時間: 2025-3-24 17:24 作者: 豐滿有漂亮 時間: 2025-3-24 21:19
https://doi.org/10.1007/978-1-4615-1491-6st to successfully incorporate a Transformer-based model to solve the unsupervised abstractive MDS task. We evaluate our approach using three real-world datasets, and we demonstrate substantial improvements in terms of evaluation metrics over state-of-the-art abstractive-based unsupervised methods.作者: 展覽 時間: 2025-3-24 23:41 作者: Charade 時間: 2025-3-25 06:03 作者: seruting 時間: 2025-3-25 09:23
C. G. Gottfries,Rolf Adolfsson,Bengt Winbladtaset with 43.02% improvement, 2.94% on RIMES dataset with 56.25% improvement and 7.35% on READ-2016 dataset with 47.27% improvement from the existing results. The character error rate and word error rate reported in this work surpass the results reported in the literature.作者: chemoprevention 時間: 2025-3-25 15:42
Arxiv Tables: Document Understanding Challenge Linking Texts and?Tablesistinguishes the dataset is that (1) the domain is science, (2) the input texts are longer than in typical Document Understanding Question Answering tasks, and (3) both the input and output contain non-standard characters used in scientific notation. For easier comparison for future research using this dataset, strong baselines are also given.作者: GROWL 時間: 2025-3-25 18:21
Text Line Detection and?Recognition of?Greek Polytonic Documentsrovide a new dataset to the public (GTLD-small) for text line detection. We evaluate our method through scenarios applied to the detection and recognition tasks, while demonstrating promising results when compared to popular commercial or open-source systems.作者: 接合 時間: 2025-3-26 00:01 作者: 無能力之人 時間: 2025-3-26 03:03
Macmillan Motor Vehicle Engineering Serieshus it is purely a structure perception approach, not dependent on the language and the quality of the text reading. We evaluate our model on invoice-like documents and the proposed method showed good results for the task of table extraction.作者: ALIEN 時間: 2025-3-26 08:14 作者: 尋找 時間: 2025-3-26 09:24
E. S. Garnett,G. Firnau,C. Nahmiasges with more accurate style realization. This loss function has the merit of applicability to various font generation models. Our experimental results show that the proposed loss function improves the quality of generated character images by several few-shot font generation models.作者: Default 時間: 2025-3-26 14:05
A Clustering Approach Combining Lines and?Text Detection for?Table Extractionhus it is purely a structure perception approach, not dependent on the language and the quality of the text reading. We evaluate our model on invoice-like documents and the proposed method showed good results for the task of table extraction.作者: 創(chuàng)造性 時間: 2025-3-26 20:03
A New Optimization Approach to Improve an Ensemble Learning Model: Application to Persian/Arabic Hanthm. The ensemble model is fed by a set of handcrafted features, including directional and intersection features, extracted from handwritten text. The proposed model is evaluated using three different datasets. Results obtained from the proposed models demonstrate higher accuracies compared to the state-of-the-art models.作者: Overstate 時間: 2025-3-27 00:36 作者: –scent 時間: 2025-3-27 04:22
Conference proceedings 2023ment 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 Workshop on Com作者: 通便 時間: 2025-3-27 07:27 作者: Exonerate 時間: 2025-3-27 13:14
C. G. Gottfries,Rolf Adolfsson,Bengt Winbladrovide a new dataset to the public (GTLD-small) for text line detection. We evaluate our method through scenarios applied to the detection and recognition tasks, while demonstrating promising results when compared to popular commercial or open-source systems.作者: extinguish 時間: 2025-3-27 16:30
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: Femish 時間: 2025-3-27 18:18 作者: 安慰 時間: 2025-3-27 22:24
Vehicle Mechanical and Electronic Systemsorrect transcription as target to the transformer. The model is trained and evaluated on such pairs of text collected from News tickers in videos and tweets from multiple News channels. A comprehensive experimental study shows significant performance improvement by introducing the proposed post-processing step.作者: 情感 時間: 2025-3-28 04:24 作者: recession 時間: 2025-3-28 09:45 作者: PLUMP 時間: 2025-3-28 11:36
https://doi.org/10.1007/978-3-031-41501-2Document Image Analysis and Recognition; Natural Language Processing; Computational Paleography; Digita作者: 娘娘腔 時間: 2025-3-28 15:39 作者: 音的強弱 時間: 2025-3-28 20:55 作者: 多產(chǎn)魚 時間: 2025-3-29 00:08
Electron Holography: AlAs/GaAs Superlatticesgnition, is ligatures. A combination of a specific two or more character sequence takes a different shape than what those characters normally look like when they appear in a similar position. Deep learning-based systems are widely used for text recognition these days. In this work, we investigate th作者: 緩解 時間: 2025-3-29 05:25
Hugh Rudnick,Constantin Velásquezble performance in addressing the task; however, most of these approaches rely on vast amounts of data from large-scale knowledge graphs or language models pretrained on voluminous corpora. In this paper, we hone in on the effective utilization of solely the knowledge supplied by a corpus to create 作者: 傲慢人 時間: 2025-3-29 10:47 作者: condescend 時間: 2025-3-29 13:32
Hugh Rudnick,Constantin Velásquezwas not left behind with first Transformer based models for DU dating from late 2019. However, the computational complexity of the self-attention operation limits their capabilities to small sequences. In this paper we explore multiple strategies to apply Transformer based models to long multi-page 作者: 表被動 時間: 2025-3-29 18:47
Final-drive/Differential and Axle Shafts,e-art results. In this paper, we propose KAP a pre-trained model adapted for the domain specificity for corporate documents. KAP takes into account the domain specificity of corporate documents and proposes a model that integrates the local context of each word (i.e the words at the top, bottom, and作者: Innovative 時間: 2025-3-29 22:58 作者: 埋伏 時間: 2025-3-30 02:42 作者: 責(zé)任 時間: 2025-3-30 07:25
Macmillan Motor Vehicle Engineering Seriess always a challenging task. On the other hand, large volumes of public training datasets related to administrative documents such as invoices are rare to find. In this work, we use Graph Attention Network model for information extraction. This type of model makes it easier to understand the mechani作者: ANT 時間: 2025-3-30 11:41
Vehicle Mechanical and Electronic Systemslity of processing them manually, the automatic processing of these documents is becoming increasingly necessary in certain sectors. However, this task remains challenging, since in most cases a text-only based parsing is not enough to fully understand the information presented through different com作者: 讓步 時間: 2025-3-30 15:37 作者: 健壯 時間: 2025-3-30 17:36
https://doi.org/10.1007/978-1-4615-1491-6nerate a coherent and fluent summary for multiple documents using natural language generation techniques. In this paper, we consider the unsupervised abstractive MDS setting where there are only documents with no ground truth summaries provided, and we propose Absformer, a new Transformer-based meth作者: 不愛防注射 時間: 2025-3-30 20:56
https://doi.org/10.1007/978-3-642-99338-1fields of research, including psychology, computer science and artificial intelligence. Automatic detection of age, gender, handedness, nationality, and qualification of writers based on handwritten documents has several real-world applications, such as forensics and psychology. This paper proposes 作者: Obscure 時間: 2025-3-31 02:37
https://doi.org/10.1007/978-3-642-99338-1ognition have been developed in the literature. This paper presents a new ensemble model based on the Feedforward Neural Networks (FFNN) to accurately recognize Persian and Arabic handwritten characters. As training and optimizing FFNN models have a significant role in obtaining optimal results, two作者: 鑲嵌細工 時間: 2025-3-31 07:28
K. Jellinger,G. P. Reynolds,P. Riedererought by the curvilinear nature of writing and lack of quality datasets. This paper solves the segmentation problem by introducing a state-of-the-art method (. (. .)) that combines a deep learning-based object detection framework (YOLO) with Hough and Affine transformation for skew correction. Howev作者: aggressor 時間: 2025-3-31 11:57 作者: ineluctable 時間: 2025-3-31 17:12
C. G. Gottfries,Rolf Adolfsson,Bengt Winbladertical Attention Network and Word Beam Search. The attention module is responsible for internal line segmentation that consequently processes a page in a line-by-line manner. At the decoding step, we have added a connectionist temporal classification-based word beam search decoder as a post-process作者: 昏迷狀態(tài) 時間: 2025-3-31 19:58
E. S. Garnett,G. Firnau,C. Nahmiasind important local parts. The local parts with larger attention are then considered important. The proposed mechanism can be trained in a quasi-self-supervised manner that requires no manual annotation other than knowing that a set of character images are from the same font, such as .. After confir作者: 失望未來 時間: 2025-3-31 22:17 作者: 痛苦一生 時間: 2025-4-1 03:16
Typefaces and?Ligatures in?Printed Arabic Text: A Deep Learning-Based OCR Perspectivegnition, is ligatures. A combination of a specific two or more character sequence takes a different shape than what those characters normally look like when they appear in a similar position. Deep learning-based systems are widely used for text recognition these days. In this work, we investigate th作者: 儀式 時間: 2025-4-1 07:01
Leveraging Knowledge Graph Embeddings to?Enhance Contextual Representations for?Relation Extractionble performance in addressing the task; however, most of these approaches rely on vast amounts of data from large-scale knowledge graphs or language models pretrained on voluminous corpora. In this paper, we hone in on the effective utilization of solely the knowledge supplied by a corpus to create
