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: 畸齒矯正學(xué) 時(shí)間: 2025-3-21 18:17
書目名稱Document Analysis and Recognition – ICDAR 2021影響因子(影響力)
書目名稱Document Analysis and Recognition – ICDAR 2021影響因子(影響力)學(xué)科排名
書目名稱Document Analysis and Recognition – ICDAR 2021網(wǎng)絡(luò)公開度
書目名稱Document Analysis and Recognition – ICDAR 2021網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Document Analysis and Recognition – ICDAR 2021被引頻次
書目名稱Document Analysis and Recognition – ICDAR 2021被引頻次學(xué)科排名
書目名稱Document Analysis and Recognition – ICDAR 2021年度引用
書目名稱Document Analysis and Recognition – ICDAR 2021年度引用學(xué)科排名
書目名稱Document Analysis and Recognition – ICDAR 2021讀者反饋
書目名稱Document Analysis and Recognition – ICDAR 2021讀者反饋學(xué)科排名
作者: 血友病 時(shí)間: 2025-3-21 21:56
Document Analysis and Recognition – ICDAR 2021978-3-030-86337-1Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: 頭腦冷靜 時(shí)間: 2025-3-22 04:29 作者: 光滑 時(shí)間: 2025-3-22 05:31
Theranostics for Breast Cancer Stem Cellss, as encountered in social networks, for detection and recognition of scene text. The proposed classifier efficiently removes non-text images from consideration, thus allowing to apply the potentially computationally heavy scene text detection and OCR on only a fraction of the images..The proposed 作者: Palate 時(shí)間: 2025-3-22 12:47 作者: vector 時(shí)間: 2025-3-22 15:00
Translational Medicine Researchifficult to use rectangular bounding boxes to detect text locations accurately. To detect multi-oriented text, rotated bounding box-based methods have been explored as an alternative. However, they are not as accurate for scene text detection as rectangular bounding box-based methods. In this paper,作者: vector 時(shí)間: 2025-3-22 20:48
Translational Research in Strokebased on abundant labeled data for model training. Obtaining text images is a relatively easy process, but labeling them is quite expensive. To alleviate the dependence on labeled data, semi-supervised learning which combines labeled and unlabeled data seems to be a reasonable solution, and is prove作者: 音樂(lè)學(xué)者 時(shí)間: 2025-3-23 00:03 作者: 口音在加重 時(shí)間: 2025-3-23 01:22 作者: Meager 時(shí)間: 2025-3-23 07:59 作者: 橫截,橫斷 時(shí)間: 2025-3-23 11:05
Stem Cell Biology and Regenerative Medicinental one-line license plate recognition and consider license plate recognition as a one-dimensional sequence recognition problem. However, for multidirectional and two-line license plates, the features of adjacent characters may mix together when directly transforming a license plate image into a on作者: Terminal 時(shí)間: 2025-3-23 17:32 作者: 小臼 時(shí)間: 2025-3-23 21:14 作者: 山羊 時(shí)間: 2025-3-24 01:16 作者: 前面 時(shí)間: 2025-3-24 02:30
Rais Ansari,Claude L. Hughes,Kazim Husainithms. Nevertheless, existing approaches often obtain inaccurate detection results, mainly due to the relatively weak ability to utilize context information and the inappropriate choice of offset references. This paper presents a novel text instance expression which integrates both foreground and ba作者: Androgen 時(shí)間: 2025-3-24 07:30
Notch Signaling in Vascular Development15 script sub-types. Ground truth is manually created by a Hebrew paleographer at a page level. In addition, we propose a patch generation tool for extracting patches that contain an approximately equal number of text lines no matter the variety of font sizes. The VML-HP dataset contains a train set作者: G-spot 時(shí)間: 2025-3-24 13:54 作者: 輪流 時(shí)間: 2025-3-24 15:31
Shalini Jadeja,Marcus Fruttigers plays an important role in creating structured, searchable text-based representations after digitizing paper-based documents for example. Traditionally, text segmentation has been approached with sub-optimal feature engineering efforts and heuristic modelling. We propose a novel supervised trainin作者: 欄桿 時(shí)間: 2025-3-24 20:03
Translational Vascular Medicine of a person, frequently used in forensic document analysis. In the writer identification and verification scenario, the number of samples available for training is not always sufficient. Thus, in this work, we investigate the impact of the increase in the number of manuscript samples on the writer 作者: 階層 時(shí)間: 2025-3-24 23:23
https://doi.org/10.1007/978-3-030-86337-1artificial intelligence; character recognition; computational linguistics; computer science; computer sy作者: STENT 時(shí)間: 2025-3-25 06:30
978-3-030-86336-4Springer Nature Switzerland AG 2021作者: Exposition 時(shí)間: 2025-3-25 09:51 作者: white-matter 時(shí)間: 2025-3-25 15:02 作者: PLUMP 時(shí)間: 2025-3-25 19:00 作者: 撕裂皮肉 時(shí)間: 2025-3-25 22:12
Fast Text vs. Non-text Classification of Imagestion is based on a block-level approach. FTC achieves 94.2% F-measure, 0.97 area under the ROC curve, and 74.8?ms and 8.6?ms inference times for CPU and GPU, respectively. A dataset of 1M images, automatically annotated with masks indicating text presence, is introduced and made public at ..作者: thrombosis 時(shí)間: 2025-3-26 02:26
FEDS - Filtered Edit Distance Surrogatevarious challenging scene text datasets such as IIIT-5K, SVT, ICDAR, SVTP, and CUTE. The proposed method provides an average improvement of . on total edit distance and an error reduction of . on accuracy.作者: modifier 時(shí)間: 2025-3-26 06:16
VML-HP: Hebrew Paleography Datasetve evaluated several deep learning classifiers on both of the test sets. The results show that convolutional networks can classify Hebrew script sub-types on a typical test set with accuracy much higher than the accuracy on the blind test.作者: Leisureliness 時(shí)間: 2025-3-26 10:30 作者: Banister 時(shí)間: 2025-3-26 15:32 作者: Hot-Flash 時(shí)間: 2025-3-26 20:46 作者: sed-rate 時(shí)間: 2025-3-26 22:02 作者: 妨礙議事 時(shí)間: 2025-3-27 02:07 作者: A簡(jiǎn)潔的 時(shí)間: 2025-3-27 05:22 作者: Cultivate 時(shí)間: 2025-3-27 11:56 作者: 急急忙忙 時(shí)間: 2025-3-27 14:27 作者: 某人 時(shí)間: 2025-3-27 18:57 作者: Encumber 時(shí)間: 2025-3-28 01:08
The Takotsubo (Broken Heart Syndrome)et AA task. Our experiments suggest that a linear classifier can achieve near perfect attribution accuracy under closed set assumption yet, the need for more robust approaches becomes evident once a large candidate pool has to be considered in the open-set classification setting.作者: 辮子帶來(lái)幫助 時(shí)間: 2025-3-28 03:48
Shalini Jadeja,Marcus Fruttigerutilizes Bidirectional Encoder Representations from Transformers (BERT) as an encoding mechanism, which feeds to several downstream layers with a final classification output layer, and even shows promise for improved results with future iterations of BERT.作者: Palliation 時(shí)間: 2025-3-28 07:41 作者: agglomerate 時(shí)間: 2025-3-28 11:17 作者: 玉米棒子 時(shí)間: 2025-3-28 15:39
SynthTIGER: Synthetic Text Image GEneratoR Towards Better Text Recognition Models the combination of synthetic datasets, MJSynth (MJ) and SynthText (ST). Our ablation study demonstrates the benefits of using sub-components of SynthTIGER and the guideline on generating synthetic text images for STR models. Our implementation is publicly available at ..作者: Debark 時(shí)間: 2025-3-28 19:28 作者: 不能約 時(shí)間: 2025-3-28 23:17 作者: 不在灌木叢中 時(shí)間: 2025-3-29 04:05 作者: ingenue 時(shí)間: 2025-3-29 07:18 作者: Herbivorous 時(shí)間: 2025-3-29 14:08 作者: Liability 時(shí)間: 2025-3-29 19:27
Fast Text vs. Non-text Classification of Imagess, as encountered in social networks, for detection and recognition of scene text. The proposed classifier efficiently removes non-text images from consideration, thus allowing to apply the potentially computationally heavy scene text detection and OCR on only a fraction of the images..The proposed 作者: harbinger 時(shí)間: 2025-3-29 22:33
Mask Scene Text Recognizer a supervised learning task of predicting text image mask into a CNN (convolutional neural network)-Transformer framework for scene text recognition. The incorporated mask predicting branch is connected in parallel with the CNN backbone, and the predicted mask is used as attention weights for the fe作者: 激勵(lì) 時(shí)間: 2025-3-30 03:29 作者: kidney 時(shí)間: 2025-3-30 06:04
Heterogeneous Network Based Semi-supervised Learning for Scene Text?Recognitionbased on abundant labeled data for model training. Obtaining text images is a relatively easy process, but labeling them is quite expensive. To alleviate the dependence on labeled data, semi-supervised learning which combines labeled and unlabeled data seems to be a reasonable solution, and is prove作者: AMEND 時(shí)間: 2025-3-30 10:12
Scene Text Detection with Scribble Lineng data. However, the annotation costs of scene text detection are huge with traditional labeling methods due to the various shapes of texts. Thus, it is practical and insightful to study simpler labeling methods without harming the detection performance. In this paper, we propose to annotate the te作者: 先鋒派 時(shí)間: 2025-3-30 14:58 作者: locus-ceruleus 時(shí)間: 2025-3-30 20:12
SynthTIGER: Synthetic Text Image GEneratoR Towards Better Text Recognition Modelsrld. Specifically, they generate multiple text images with diverse backgrounds, font styles, and text shapes and enable STR models to learn visual patterns that might not be accessible from manually annotated data. In this paper, we introduce a new synthetic text image generator, SynthTIGER, by anal作者: 驕傲 時(shí)間: 2025-3-30 22:41 作者: 吹牛需要藝術(shù) 時(shí)間: 2025-3-31 04:05
A Multi-level Progressive Rectification Mechanism for Irregular Scene Text Recognition perform rectification at the image level once. This may be insufficient for complicated deformations. To this end, we propose a multi-level progressive rectification mechanism, which consists of global and local rectification modules at the image level and a refinement rectification module at the f作者: 有權(quán)威 時(shí)間: 2025-3-31 06:08 作者: Encumber 時(shí)間: 2025-3-31 11:35
FEDS - Filtered Edit Distance Surrogate from self-paced learning and filters out the training examples that are hard for the surrogate. The filtering is performed by judging the quality of the approximation, using a ramp function, enabling end-to-end training. Following the literature, the experiments are conducted in a post-tuning setup作者: Fierce 時(shí)間: 2025-3-31 17:13 作者: 初次登臺(tái) 時(shí)間: 2025-3-31 18:38
