標(biāo)題: Titlebook: Document Analysis and Recognition - ICDAR 2024; 18th International C Elisa H. Barney Smith,Marcus Liwicki,Liangrui Peng Conference proceedi [打印本頁(yè)] 作者: Coenzyme 時(shí)間: 2025-3-21 17:19
書(shū)目名稱Document Analysis and Recognition - ICDAR 2024影響因子(影響力)
書(shū)目名稱Document Analysis and Recognition - ICDAR 2024影響因子(影響力)學(xué)科排名
書(shū)目名稱Document Analysis and Recognition - ICDAR 2024網(wǎng)絡(luò)公開(kāi)度
書(shū)目名稱Document Analysis and Recognition - ICDAR 2024網(wǎng)絡(luò)公開(kāi)度學(xué)科排名
書(shū)目名稱Document Analysis and Recognition - ICDAR 2024被引頻次
書(shū)目名稱Document Analysis and Recognition - ICDAR 2024被引頻次學(xué)科排名
書(shū)目名稱Document Analysis and Recognition - ICDAR 2024年度引用
書(shū)目名稱Document Analysis and Recognition - ICDAR 2024年度引用學(xué)科排名
書(shū)目名稱Document Analysis and Recognition - ICDAR 2024讀者反饋
書(shū)目名稱Document Analysis and Recognition - ICDAR 2024讀者反饋學(xué)科排名
作者: 規(guī)章 時(shí)間: 2025-3-21 22:43 作者: 細(xì)節(jié) 時(shí)間: 2025-3-22 00:23
Document Specular Highlight Removal with?Coarse-to-Fine Strategyetween the ground-truth and the CP-predicted image. Experimental results on four public benchmark images demonstrate that our method surpasses state-of-the-art methods in the task of highlight removal.作者: WITH 時(shí)間: 2025-3-22 06:31
KVP10k : A Comprehensive Dataset for?Key-Value Pair Extraction in?Business Documents0k , a new dataset and benchmark specifically designed for KVP extraction. The dataset contains 10707richly annotated images. In our benchmark, we also introduce a new challenging task that combines elements of KIE as well as KVP in a single task. KVP10k sets itself apart with its extensive diversit作者: Nutrient 時(shí)間: 2025-3-22 09:02
Context-Aware Confidence Estimation for?Rejection in?Handwritten Chinese Text Recognitionand binary geometric features. Experimental evaluations on the CASIA-HWDB and ICDAR2013 datasets demonstrate that our method can significantly improve the rejection performance in respect of low error rate at moderate rejection rate. The re-trained classifier, the linguistic context and the geometri作者: Cosmopolitan 時(shí)間: 2025-3-22 13:46
Radical Similarity Based Model Optimization and?Post-correction for?Chinese Character Recognitionhe potential error recognition results, offering a low-cost yet effective solution. Experimental results on different radical-based CCR models and datasets demonstrate the effectiveness and robustness of our proposed method.作者: Cosmopolitan 時(shí)間: 2025-3-22 19:00
Puzzle Pieces Picker: Deciphering Ancient Chinese Characters with?Radical Reconstruction promising insights, underscoring the potential and effectiveness of our approach in deciphering the intricacies of ancient Chinese scripts. Through this novel dataset and methodology, we aim to bridge the gap between traditional philology and modern document analysis techniques, offering new insigh作者: esculent 時(shí)間: 2025-3-22 22:04 作者: 絆住 時(shí)間: 2025-3-23 05:04
GraphMLLM: A Graph-Based Multi-level Layout Language-Independent Model for?Document Understandingmprove the performance of language-independent document pre-trained model. Experimental results show that compared with previous state-of-the-art methods, GraphMLLM yields higher performance on downstream visual information extraction (VIE) tasks after pre-training on less documents. Code and model 作者: Directed 時(shí)間: 2025-3-23 07:40
EntityLayout: Entity-Level Pre-training Language Model for?Semantic Entity Recognition and?Relation ental results on public datasets FUNSD and CORD demonstrate that the proposed EntityLayout achieves competitive performance in SER and state-of-the-art performance in RE, i.e., SER F1 scores of 0.9108 and 0.9650, respectively, RE F1 scores of 0.8212 and 0.9898, respectively.作者: MAL 時(shí)間: 2025-3-23 09:49
n and recognition using the same backbone, with the detection branch based on multiple deep layers and the recognition branch based on multiple shallow layers, thereby constructing an end-to-end detection and recognition network. Comparative experiments on CCPD and RodoSol datasets validate that our作者: Resistance 時(shí)間: 2025-3-23 16:29
Weibo Luo,Yingfei Wang,Georg Reiser*etween the ground-truth and the CP-predicted image. Experimental results on four public benchmark images demonstrate that our method surpasses state-of-the-art methods in the task of highlight removal.作者: 媒介 時(shí)間: 2025-3-23 19:05 作者: 機(jī)械 時(shí)間: 2025-3-24 01:41 作者: 腐蝕 時(shí)間: 2025-3-24 05:03
he potential error recognition results, offering a low-cost yet effective solution. Experimental results on different radical-based CCR models and datasets demonstrate the effectiveness and robustness of our proposed method.作者: acclimate 時(shí)間: 2025-3-24 08:33 作者: MORT 時(shí)間: 2025-3-24 10:52 作者: panorama 時(shí)間: 2025-3-24 18:15 作者: gnarled 時(shí)間: 2025-3-24 19:32 作者: 即席演說(shuō) 時(shí)間: 2025-3-25 00:23
0302-9743 ICDAR 2024, held in Athens, Greece, during August 30–September 4, 2024..The total of 144 full papers presented in these proceedings were carefully selected from 263 submissions..The papers reflect topics such as: Document image processing; physical and logical layout analysis; text and symbol recog作者: 財(cái)產(chǎn) 時(shí)間: 2025-3-25 05:42
Conference proceedings 2024ndwriting recognition; document analysis systems; document classification; indexing and retrieval of documents; document synthesis; extracting document semantics; NLP for document understanding; office automation; graphics recognition; human document interaction; document representation modeling and much more...?.作者: 編輯才信任 時(shí)間: 2025-3-25 08:28
0302-9743 ng document semantics; NLP for document understanding; office automation; graphics recognition; human document interaction; document representation modeling and much more...?.978-3-031-70532-8978-3-031-70533-5Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: Indent 時(shí)間: 2025-3-25 14:14
https://doi.org/10.1007/978-3-662-13130-5ds. Experiments using Convolutional Neural Networks showed that using class decomposition significantly improves the classification performance that can be achieved, without causing information loss, as it is the case with other class imbalance data sampling approaches.作者: Malfunction 時(shí)間: 2025-3-25 17:07
https://doi.org/10.1007/978-3-662-10287-9o verify OVD shapes and dynamics with very little supervision, this work opens the way towards the use of massive amounts of unlabeled data to build robust remote identity document verification systems on commodity smartphones. Code is available at ..作者: BLAND 時(shí)間: 2025-3-25 23:35 作者: Heretical 時(shí)間: 2025-3-26 00:22
A Multiclass Imbalanced Dataset Classification of?Symbols from?Piping and?Instrumentation Diagramsds. Experiments using Convolutional Neural Networks showed that using class decomposition significantly improves the classification performance that can be achieved, without causing information loss, as it is the case with other class imbalance data sampling approaches.作者: 破譯 時(shí)間: 2025-3-26 05:24 作者: 欄桿 時(shí)間: 2025-3-26 09:58
One-Shot Transformer-Based Framework for?Visually-Rich Document Understandingto the full set of labeled entities in the public SROIE datasets. We have also gathered and annotated the public RVL-CDIP and invoice datasets to showcase the generalization of our OTER models for the EE task across a wide range of document templates, containing both single and multiple-region fields.作者: 鍵琴 時(shí)間: 2025-3-26 12:56
Conference proceedings 20244, held in Athens, Greece, during August 30–September 4, 2024..The total of 144 full papers presented in these proceedings were carefully selected from 263 submissions..The papers reflect topics such as: Document image processing; physical and logical layout analysis; text and symbol recognition; ha作者: Fibrillation 時(shí)間: 2025-3-26 20:17
Beta-delayed (multi-)particle decay studies, to document resolution variability. Moreover, the few-shot approach allow the model to perform well even for unseen class of documents. Preliminary results on the SIDTD and Findit datasets show good performance of this model for this task.作者: 揉雜 時(shí)間: 2025-3-26 20:58
https://doi.org/10.1007/978-1-4615-3296-5ate balanced, diverse, and accurately annotated slide data. We demonstrate SlideCraft’s efficacy in enhancing slide layout analysis algorithms, focusing on its capability to improve dataset quality and object detection performance.作者: 違反 時(shí)間: 2025-3-27 03:11 作者: Irritate 時(shí)間: 2025-3-27 07:19 作者: tattle 時(shí)間: 2025-3-27 10:48
Recurrent Few-Shot Model for?Document Verification to document resolution variability. Moreover, the few-shot approach allow the model to perform well even for unseen class of documents. Preliminary results on the SIDTD and Findit datasets show good performance of this model for this task.作者: 骯臟 時(shí)間: 2025-3-27 13:46
SlideCraft: Synthetic Slides Generation for?Robust Slide Analysisate balanced, diverse, and accurately annotated slide data. We demonstrate SlideCraft’s efficacy in enhancing slide layout analysis algorithms, focusing on its capability to improve dataset quality and object detection performance.作者: 擁護(hù)者 時(shí)間: 2025-3-27 18:41
Visual Prompt Learning for?Chinese Handwriting Recognitionith the embeddings of previously predicted text to guide the decoding process. Experiments conducted on the SCUT-HCCDoc, SCUT-EPT and CASIA-HWDB Chinese handwriting datasets validate the effectiveness of the proposed methods.作者: lobster 時(shí)間: 2025-3-28 00:23 作者: 披肩 時(shí)間: 2025-3-28 04:44
A Multiclass Imbalanced Dataset Classification of?Symbols from?Piping and?Instrumentation Diagramsest in developing solutions for processing and analysing these diagrams using wide range of image-processing and computer vision techniques. In this paper, we first, present a new multiclass imbalanced dataset of symbols extracted from Piping and Instrumentation Diagrams (P&IDs). The dataset contain作者: 公共汽車 時(shí)間: 2025-3-28 10:06
Weakly Supervised Training for?Hologram Verification in?Identity Documents Our method processes video clips captured with smartphones under common lighting conditions, and is evaluated on two public datasets: MIDV-HOLO and MIDV-2020. Thanks to a weakly-supervised training, we optimize a feature extraction and decision pipeline which achieves a new leading performance on M作者: Mindfulness 時(shí)間: 2025-3-28 10:41
Multi-task Learning for?License Plate Recognition in?Unconstrained Scenariosstudies have treated license plate detection and recognition as separate tasks, resulting in inefficiencies and error accumulation. To address these challenges, we propose an end-to-end method for license plate detection and recognition using multi-task learning. Firstly, we introduce two parallel b作者: Biguanides 時(shí)間: 2025-3-28 18:26
Recurrent Few-Shot Model for?Document Verificationved problem. There are several factors that negatively impact their performance, including low-resolution images and videos and a lack of sufficient data to train the models. This task is particularly challenging when dealing with unseen class of ID, or travel, documents. In this paper we address th作者: Valves 時(shí)間: 2025-3-28 21:21
Document Specular Highlight Removal with?Coarse-to-Fine Strategyrecursor to guide the model in achieving better removal of specular highlights. This paper introduces a novel highlight removal model, which presents an efficient end-to-end deep learning framework designed to automatically remove specular highlights from a single image. Our architecture comprises t作者: 植物茂盛 時(shí)間: 2025-3-29 01:19 作者: constellation 時(shí)間: 2025-3-29 06:44
KVP10k : A Comprehensive Dataset for?Key-Value Pair Extraction in?Business Documentsdomains. This effort has attracted substantial interest from both industry and academy, highlighting its significance in the current technological landscape. Most datasets in this area are primarily focused on Key Information Extraction (KIE), where the extraction process revolves around extracting 作者: Communicate 時(shí)間: 2025-3-29 08:07 作者: CURL 時(shí)間: 2025-3-29 14:17 作者: BILIO 時(shí)間: 2025-3-29 17:01
Radical Similarity Based Model Optimization and?Post-correction for?Chinese Character Recognitionsed methods, Chinese characters are described as combinations of structures and radicals, and character recognition is achieved by the proper identifications of these components. However, there are visual similarities among radicals, leading to the ambiguity problem for CCR, which is not fully utili作者: 小隔間 時(shí)間: 2025-3-29 20:51
Puzzle Pieces Picker: Deciphering Ancient Chinese Characters with?Radical Reconstructionf Oracle Bone Inscriptions (OBI) remain undeciphered, making it one of the global challenges in the field of paleography today. This paper introduces a novel approach, namely Puzzle Pieces Picker (P.), to decipher these enigmatic characters through radical reconstruction. We deconstruct OBI into fou作者: TRAWL 時(shí)間: 2025-3-30 00:36
Light-Weight Multi-modality Feature Fusion Network for?Visually-Rich Document Understandingmage. Recent transformer-based architectures enable an effective fusion of these features, showing great performance on the EE task. However, these models are heavy, leading to substantially high training cost and low inference speed. Thus, we propose a light-weight transformer-based model (named LM作者: Insensate 時(shí)間: 2025-3-30 07:57 作者: 增減字母法 時(shí)間: 2025-3-30 09:19
GraphMLLM: A Graph-Based Multi-level Layout Language-Independent Model for?Document Understandingiversity of document languages and structures, there is still room to better model various document layouts while efficiently utilizing the pre-trained language models. To this goal, this paper proposes a Graph-based Multi-level Layout Language-independent Model (GraphMLLM) which uses dual-stream st作者: 異教徒 時(shí)間: 2025-3-30 16:03 作者: 飛鏢 時(shí)間: 2025-3-30 18:40 作者: 臭了生氣 時(shí)間: 2025-3-30 22:30 作者: 卜聞 時(shí)間: 2025-3-31 04:47
https://doi.org/10.1007/978-3-662-10287-9 Our method processes video clips captured with smartphones under common lighting conditions, and is evaluated on two public datasets: MIDV-HOLO and MIDV-2020. Thanks to a weakly-supervised training, we optimize a feature extraction and decision pipeline which achieves a new leading performance on M作者: anus928 時(shí)間: 2025-3-31 08:48 作者: 旁觀者 時(shí)間: 2025-3-31 13:10 作者: vitreous-humor 時(shí)間: 2025-3-31 13:45 作者: 茁壯成長(zhǎng) 時(shí)間: 2025-3-31 18:45
https://doi.org/10.1007/978-1-4615-3296-5ound that current slide datasets contain inconsistencies, mislabels, and incomplete annotations. Using them as a basis for developing deep learning-based slide analysis models could lead to models that are not robust and suboptimal. Addressing these challenges, we introduce SlideCraft, a tool for cr
