標(biāo)題: Titlebook: Document Analysis and Recognition – ICDAR 2021; 16th International C Josep Lladós,Daniel Lopresti,Seiichi Uchida Conference proceedings 202 [打印本頁(yè)] 作者: deteriorate 時(shí)間: 2025-3-21 16:52
書(shū)目名稱Document Analysis and Recognition – ICDAR 2021影響因子(影響力)
書(shū)目名稱Document Analysis and Recognition – ICDAR 2021影響因子(影響力)學(xué)科排名
書(shū)目名稱Document Analysis and Recognition – ICDAR 2021網(wǎng)絡(luò)公開(kāi)度
書(shū)目名稱Document Analysis and Recognition – ICDAR 2021網(wǎng)絡(luò)公開(kāi)度學(xué)科排名
書(shū)目名稱Document Analysis and Recognition – ICDAR 2021被引頻次
書(shū)目名稱Document Analysis and Recognition – ICDAR 2021被引頻次學(xué)科排名
書(shū)目名稱Document Analysis and Recognition – ICDAR 2021年度引用
書(shū)目名稱Document Analysis and Recognition – ICDAR 2021年度引用學(xué)科排名
書(shū)目名稱Document Analysis and Recognition – ICDAR 2021讀者反饋
書(shū)目名稱Document Analysis and Recognition – ICDAR 2021讀者反饋學(xué)科排名
作者: Myofibrils 時(shí)間: 2025-3-21 22:40 作者: nuclear-tests 時(shí)間: 2025-3-22 01:38
Currently Available Systems: METALare generated using an automated multi-directional steerable filters approach. The generated wall masks are then validated and corrected manually. We validate our approach of wall-mask generation in state-of-the-art modern datasets. Finally we propose a U-net based convolutional framework for wall d作者: TEN 時(shí)間: 2025-3-22 06:08
Children in Translocal Familiess on cTDaR 2019 Archival dataset show that our method can outperform the baselines and achieve new state-of-the-art performance, which demonstrates the effectiveness and superiority of the proposed method.作者: 違抗 時(shí)間: 2025-3-22 09:49 作者: gerrymander 時(shí)間: 2025-3-22 13:53
The Abject, Murder, and Sex in ,+ntic features are extracted using a . network, which are . fused to make full use of complementary information. Finally, given component candidates, a . based on graph neural network is incorported to model relations between components and output final results. On three popular benchmarks, VSR outpe作者: gerrymander 時(shí)間: 2025-3-22 18:03
https://doi.org/10.1007/978-981-10-8609-0d applications. The core . library comes with a set of simple and intuitive interfaces for applying and customizing DL models for layout detection, character recognition, and many other document processing tasks. To promote extensibility, . also incorporates a community platform for sharing both pre作者: 生氣的邊緣 時(shí)間: 2025-3-22 23:32
https://doi.org/10.1007/978-94-007-2315-3ression algorithms can be successfully applied for the task of document image classification. We further analyze the impact of model compression on network outputs and highlight the discrepancy that arises during the compression process. Building on recent findings in this direction, we employ a pri作者: DEAF 時(shí)間: 2025-3-23 03:37 作者: 自制 時(shí)間: 2025-3-23 06:13
Translocality in Contemporary City Novelsof the proposed model on the three datasets: IAM Handwriting, Rimes, and TUAT Kondate. The experimental results show that the proposed model achieves similar or better accuracy when compared to state-of-the-art models in all datasets.作者: bonnet 時(shí)間: 2025-3-23 11:50 作者: 原來(lái) 時(shí)間: 2025-3-23 17:07 作者: Compatriot 時(shí)間: 2025-3-23 21:24 作者: 擴(kuò)張 時(shí)間: 2025-3-24 00:18 作者: 肌肉 時(shí)間: 2025-3-24 03:51 作者: Myelin 時(shí)間: 2025-3-24 10:29
LGPMA: Complicated Table Structure Recognition with Local and Global Pyramid Mask Alignmentning mechanism in both the local and global feature maps. It allows the predicted boundaries of bounding boxes to break through the limitation of original proposals. A pyramid mask re-scoring module is then integrated to compromise the local and global information and refine the predicted boundaries作者: 小教堂 時(shí)間: 2025-3-24 11:57 作者: nutrition 時(shí)間: 2025-3-24 17:17
: A Unified Toolkit for Deep Learning Based Document Image Analysisd applications. The core . library comes with a set of simple and intuitive interfaces for applying and customizing DL models for layout detection, character recognition, and many other document processing tasks. To promote extensibility, . also incorporates a community platform for sharing both pre作者: 惡意 時(shí)間: 2025-3-24 22:00
Understanding and Mitigating the Impact of Model Compression for Document Image Classificationression algorithms can be successfully applied for the task of document image classification. We further analyze the impact of model compression on network outputs and highlight the discrepancy that arises during the compression process. Building on recent findings in this direction, we employ a pri作者: 欺騙世家 時(shí)間: 2025-3-25 02:41 作者: fodlder 時(shí)間: 2025-3-25 06:31 作者: RAFF 時(shí)間: 2025-3-25 09:53
Mix-Up Augmentation for Oracle Character Recognition with Imbalanced Data Distributionamework with both the softmax loss and triplet loss on the augmented samples which proves able to improve the classification accuracy further. We conduct extensive evaluations w.r.t. both total class accuracy and average class accuracy on three benchmark datasets (i.e., Oracle-20K, Oracle-AYNU and O作者: 顛簸下上 時(shí)間: 2025-3-25 15:15
https://doi.org/10.1007/978-3-319-89734-9, which is out of scope for other graph-based methods in the literature. We investigate two variants of graph convolutional layers and show that learning improves performances considerably on two popular graph-based word spotting benchmarks.作者: Adjourn 時(shí)間: 2025-3-25 17:34
Children in Translocal Familiesgenerating images of promising visual quality, we are able to improve classification performance by augmenting original data with generated samples. Additionally, we demonstrate that our approach is applicable to other domains as well, like digit generation in house number signs.作者: oxidant 時(shí)間: 2025-3-25 23:23 作者: 單片眼鏡 時(shí)間: 2025-3-26 02:19
Graph Convolutional Neural Networks for Learning Attribute Representations for Word Spotting, which is out of scope for other graph-based methods in the literature. We investigate two variants of graph convolutional layers and show that learning improves performances considerably on two popular graph-based word spotting benchmarks.作者: 外形 時(shí)間: 2025-3-26 07:24
Context Aware Generation of Cuneiform Signsgenerating images of promising visual quality, we are able to improve classification performance by augmenting original data with generated samples. Additionally, we demonstrate that our approach is applicable to other domains as well, like digit generation in house number signs.作者: prediabetes 時(shí)間: 2025-3-26 08:56
Handwritten Text Recognition with Convolutional Prototype Network and Most Aligned Frame Based CTC Tors in decoding. Experiments of handwritten text recognition on four benchmark datasets of different languages show that the proposed method consistently improves the accuracy and alignment of CTC-based text recognition baseline.作者: originality 時(shí)間: 2025-3-26 16:15 作者: compose 時(shí)間: 2025-3-26 20:48
M. Kaltenbach,G. Kober,D. Schererin time if more information is needed. Moreover our system is end-to-end trainable, OLT-C3D and the temporal reject system are jointly trained to optimize the earliness of the decision. Our approach achieves superior performances on two complementary and freely available datasets: ILGDB and MTGSetB.作者: 骨 時(shí)間: 2025-3-27 00:59
Competition and Collaboration in Document Analysis and Recognitioncified tasks. We comment on the?~?100 citations garnered by these contests over the intervening 3.5?years. Finally, in what we consider a logical sequel, we speculate on the possibility of an alternative model of small-scale, short-range communal research based on collaboration that seems to offer benefits competitions cannot capture.作者: generic 時(shí)間: 2025-3-27 02:50
Online Spatio-temporal 3D Convolutional Neural Network for Early Recognition of Handwritten Gesturesin time if more information is needed. Moreover our system is end-to-end trainable, OLT-C3D and the temporal reject system are jointly trained to optimize the earliness of the decision. Our approach achieves superior performances on two complementary and freely available datasets: ILGDB and MTGSetB.作者: 斗爭(zhēng) 時(shí)間: 2025-3-27 08:49 作者: condemn 時(shí)間: 2025-3-27 10:20 作者: Enervate 時(shí)間: 2025-3-27 13:42
0302-9743 istorical document analysis, document analysis systems, handwriting recognition, scene text detection and recognition, document image processing, natural language processing (NLP) for document understanding, and graphics, diagram and math recognition..978-3-030-86548-1978-3-030-86549-8Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: osculate 時(shí)間: 2025-3-27 18:12
0302-9743 Conference on Document Analysis and Recognition, ICDAR 2021, held in Lausanne, Switzerland in September 2021.?The 182 full papers were carefully reviewed and selected from 340 submissions, and are presented with 13 competition reports..The papers are organized into the following topical sections: h作者: 使人入神 時(shí)間: 2025-3-28 01:25 作者: 反復(fù)拉緊 時(shí)間: 2025-3-28 03:52
978-3-030-86548-1Springer Nature Switzerland AG 2021作者: cacophony 時(shí)間: 2025-3-28 07:12 作者: 經(jīng)典 時(shí)間: 2025-3-28 11:25
Document access — Networks and Convertersns contain densely laid out, highly irregular and overlapping multi-class region instances with large range in aspect ratio. Fully automatic boundary estimation approaches tend to be data intensive, cannot handle variable-sized images and produce sub-optimal results for aforementioned images. To add作者: 杠桿 時(shí)間: 2025-3-28 17:24 作者: laxative 時(shí)間: 2025-3-28 20:36 作者: cathartic 時(shí)間: 2025-3-29 02:43
https://doi.org/10.1007/978-3-319-89734-9fferent learning-free document analysis tasks. While machine learning is rather unexplored for graph representations, geometric deep learning offers a novel framework that allows for convolutional neural networks similar to the image domain. In this work, we show that the concept of attribute predic作者: 館長(zhǎng) 時(shí)間: 2025-3-29 07:05 作者: 疏忽 時(shí)間: 2025-3-29 11:07 作者: 遺留之物 時(shí)間: 2025-3-29 14:29 作者: 不持續(xù)就爆 時(shí)間: 2025-3-29 19:04 作者: Dysplasia 時(shí)間: 2025-3-29 22:11
https://doi.org/10.1007/978-981-10-8609-0 easily deployed in production and extended for further investigation. However, various factors like loosely organized codebases and sophisticated model configurations complicate the easy reuse of important innovations by a wide audience. Though there have been on-going efforts to improve reusabilit作者: evaculate 時(shí)間: 2025-3-29 23:59
https://doi.org/10.1007/978-94-007-2315-3ion is a common process in business workflows, there is a dire need of analyzing the potential of compressed models for the task of document image classification. Surprisingly, no such analysis has been done in the past. Furthermore, once a compressed model is obtained using a particular compression作者: cogitate 時(shí)間: 2025-3-30 04:42 作者: expdient 時(shí)間: 2025-3-30 12:13 作者: yohimbine 時(shí)間: 2025-3-30 13:18 作者: Commonplace 時(shí)間: 2025-3-30 18:37 作者: Campaign 時(shí)間: 2025-3-30 22:05
M. Kaltenbach,G. Kober,D. Schererew architecture based on a 3D Convolutional Neural Network (3D CNN) to address the complex task of early recognition of 2D handwritten gestures in real time. The input signal of the gesture is translated into an image sequence along time with the trajectory history. The image sequence is passed into
