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] 作者: 召喚 時(shí)間: 2025-3-21 18:09
書目名稱Document Analysis and Recognition - ICDAR 2024影響因子(影響力)
書目名稱Document Analysis and Recognition - ICDAR 2024影響因子(影響力)學(xué)科排名
書目名稱Document Analysis and Recognition - ICDAR 2024網(wǎng)絡(luò)公開度
書目名稱Document Analysis and Recognition - ICDAR 2024網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Document Analysis and Recognition - ICDAR 2024被引頻次
書目名稱Document Analysis and Recognition - ICDAR 2024被引頻次學(xué)科排名
書目名稱Document Analysis and Recognition - ICDAR 2024年度引用
書目名稱Document Analysis and Recognition - ICDAR 2024年度引用學(xué)科排名
書目名稱Document Analysis and Recognition - ICDAR 2024讀者反饋
書目名稱Document Analysis and Recognition - ICDAR 2024讀者反饋學(xué)科排名
作者: SLUMP 時(shí)間: 2025-3-21 23:45
Geometric-Aware Control in?Diffusion Model for?Handwritten Chinese Font Generatione control process. Third, the geometric information such as the corner points of a target character is utilized to weight the image reconstruction loss function for better control of character shape. The effectiveness of the proposed method for handwritten Chinese font generation is validated on the作者: 巨碩 時(shí)間: 2025-3-22 00:54
Learning to?Kern: Set-Wise Estimation of?Optimal Letter Spaceertain font. Among the two models, the set-wise model is not only more efficient but also more accurate because its internal self-attention mechanism allows for more consistent kerning for all letters. Experimental results on about 2500 Google fonts and their quantitative and qualitative analyses sh作者: LURE 時(shí)間: 2025-3-22 04:51 作者: KEGEL 時(shí)間: 2025-3-22 08:49 作者: 信條 時(shí)間: 2025-3-22 13:59 作者: 信條 時(shí)間: 2025-3-22 17:18
Handwritten Document Recognition Using Pre-trained Vision Transformersearch, implementing freezing strategies, utilizing gradient accumulation, and employing hard negative mining techniques. This analysis demonstrates that our proposed efficient fine-tuning workflow significantly mitigates error rates and enhances inference speed, surpassing state-of-the-art results a作者: BLUSH 時(shí)間: 2025-3-23 01:17
Janus-Faced Handwritten Signature Attack: A?Clash Between a?Handwritten Signature Duplicator and?a?Whe test stage. These profiles advance the threat level to the SigmML verifier by refining the output of the duplicator with a quality control mechanism which intuitively adapts the a-priori knowledge?of the intra-variability of each writer. In our experiments,?we considered signatures written in var作者: Irrigate 時(shí)間: 2025-3-23 01:47 作者: Lumbar-Spine 時(shí)間: 2025-3-23 07:24 作者: Initial 時(shí)間: 2025-3-23 09:47
Content-Based Similarity for Automatic Scoring of Handwritten Descriptive Answersity between the recognized candidates of an answer and the expected answers. According to the computed similarity, it scores the answers as correct, wrong, or rejected. Human scorers should score false negative answers that are likely claimed by students and rejected answers. The experiment suggests作者: TIA742 時(shí)間: 2025-3-23 14:13 作者: 變形 時(shí)間: 2025-3-23 20:59 作者: 紅潤(rùn) 時(shí)間: 2025-3-24 02:03 作者: SCORE 時(shí)間: 2025-3-24 02:52
https://doi.org/10.1007/978-3-322-95719-1een book genres and font impressions on real book cover images; it is important to note that this analysis is only possible with our impression estimation method. The analysis reveals various trends in the correlation between them—this fact supports a hypothesis that book cover designers carefully c作者: 施舍 時(shí)間: 2025-3-24 10:08
Immer wieder einmalig. Eventtourismus, We further introduce text edge manipulation as an intuitive and customizable way to produce texts with complex effects such as “shadows” and “reflections”. Finally, with the proposed system, we successfully add and modify texts on a predefined background while preserving its overall coherence.作者: 吸氣 時(shí)間: 2025-3-24 14:31 作者: 幾何學(xué)家 時(shí)間: 2025-3-24 18:05 作者: 巫婆 時(shí)間: 2025-3-24 19:37 作者: 鋼筆尖 時(shí)間: 2025-3-25 00:44
https://doi.org/10.1007/978-3-211-33032-6on data-free knowledge transfer learning. Firstly,?we generate inverted examples with the same distribution as the?real examples. Then, we complement a feature space based on real?data using synthetic data while minimizing the divergence in distribution between the representations provided by these 作者: Proclaim 時(shí)間: 2025-3-25 05:01 作者: Irrepressible 時(shí)間: 2025-3-25 10:37
https://doi.org/10.1007/978-3-662-10587-0ity between the recognized candidates of an answer and the expected answers. According to the computed similarity, it scores the answers as correct, wrong, or rejected. Human scorers should score false negative answers that are likely claimed by students and rejected answers. The experiment suggests作者: Torrid 時(shí)間: 2025-3-25 15:01 作者: 設(shè)想 時(shí)間: 2025-3-25 17:04
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: Minatory 時(shí)間: 2025-3-25 21:55
0302-9743 ng document semantics; NLP for document understanding; office automation; graphics recognition; human document interaction; document representation modeling and much more...?..?.978-3-031-70535-9978-3-031-70536-6Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: 挑剔為人 時(shí)間: 2025-3-26 02:55
tween fonts and impressions through co-embedding. The results indicate that Impression-CLIP achieves better retrieval accuracy than the state-of-the-art method. Additionally, our model shows the robustness to noise and missing tags.作者: 離開就切除 時(shí)間: 2025-3-26 06:05
on HisFrag20, . outperforms related work with a mAP of 57.2% - a margin of 11.6% to the current state of the art, showcasing its robustness on challenging data, and is competitive on even small datasets, e.g. GRK-Papyri, where we achieve a Top-1 accuracy of 58.0%.作者: SCORE 時(shí)間: 2025-3-26 11:33
,Staat?– Gesellschaft?– Individuum, and in cross-collection scenarios. Our results report interesting conclusions about the calibration of HTR, highlighting their strengths, weaknesses, and the extent to which the considered strategies improve results.作者: 阻撓 時(shí)間: 2025-3-26 14:57 作者: 參考書目 時(shí)間: 2025-3-26 16:56
SAGHOG: Self-supervised Autoencoder for?Generating HOG Features for?Writer Retrievalon HisFrag20, . outperforms related work with a mAP of 57.2% - a margin of 11.6% to the current state of the art, showcasing its robustness on challenging data, and is competitive on even small datasets, e.g. GRK-Papyri, where we achieve a Top-1 accuracy of 58.0%.作者: headway 時(shí)間: 2025-3-27 01:03
Analysis of?the?Calibration of?Handwriting Text Recognition Models and in cross-collection scenarios. Our results report interesting conclusions about the calibration of HTR, highlighting their strengths, weaknesses, and the extent to which the considered strategies improve results.作者: intolerance 時(shí)間: 2025-3-27 05:09 作者: 浪費(fèi)時(shí)間 時(shí)間: 2025-3-27 08:02
Paul Kuff,Karl Schwalbenhofer,Alice Strohmpendent metrics tailored to Information Extraction evaluation in handwritten documents. In our experimentation,?we perform an in-depth analysis of the behavior of the metrics?to recommend what we consider to be the minimal set of metrics?to evaluate a task correctly.作者: 悄悄移動(dòng) 時(shí)間: 2025-3-27 12:13 作者: diathermy 時(shí)間: 2025-3-27 16:41
Reading Order Independent Metrics for?Information Extraction in?Handwritten Documentspendent metrics tailored to Information Extraction evaluation in handwritten documents. In our experimentation,?we perform an in-depth analysis of the behavior of the metrics?to recommend what we consider to be the minimal set of metrics?to evaluate a task correctly.作者: Implicit 時(shí)間: 2025-3-27 20:23 作者: BILL 時(shí)間: 2025-3-27 22:58
Magnesium Role in Health and Longevity,e to make temporal neural networks more robust to adversarial attacks. The proposed method is evaluated using online handwritten characters and against four state-of-the-art adversarial attacks. We demonstrate that the nontraditional use of TTA can be used to protect against these attacks for almost no cost.作者: 兇兆 時(shí)間: 2025-3-28 05:46
Test Time Augmentation as?a?Defense Against Adversarial Attacks on?Online Handwritinge to make temporal neural networks more robust to adversarial attacks. The proposed method is evaluated using online handwritten characters and against four state-of-the-art adversarial attacks. We demonstrate that the nontraditional use of TTA can be used to protect against these attacks for almost no cost.作者: Estrogen 時(shí)間: 2025-3-28 06:27 作者: PHAG 時(shí)間: 2025-3-28 12:03
Geometric-Aware Control in?Diffusion Model for?Handwritten Chinese Font Generations have shown promising performance on different tasks including Chinese font generation. However, most of the current diffusion model-based Chinese font generation methods focus on generating printed Chinese character images and pay insufficient attention to the geometric characteristics of characte作者: 忘恩負(fù)義的人 時(shí)間: 2025-3-28 14:35 作者: VAN 時(shí)間: 2025-3-28 19:20
Font Impression Estimation in?the?Wildssions and a convolutional neural network (CNN) framework for this task. However, impressions attached to individual fonts are often missing and noisy because of the subjective characteristic of font impression annotation. To realize stable impression estimation even with such a dataset, we propose 作者: 肥料 時(shí)間: 2025-3-29 00:00
Typographic Text Generation with?Off-the-Shelf Diffusion Modelted texts render them insufficient in the realm of typographic design. This paper proposes a typographic text generation system to add and modify text on typographic designs while specifying font styles, colors, and text effects. The proposed system is a novel combination of two off-the-shelf method作者: 動(dòng)物 時(shí)間: 2025-3-29 05:59
Impression-CLIP: Contrastive Shape-Impression Embedding for?Fontsression is weak and unstable because impressions are subjective. To capture such weak and unstable cross-modal correlation between font shapes and their impressions, we propose Impression-CLIP, which is a novel machine-learning model based on CLIP (Contrastive Language-Image Pre-training). By using 作者: 敲竹杠 時(shí)間: 2025-3-29 08:17 作者: hypotension 時(shí)間: 2025-3-29 14:52
Script Identification in?the?Wild with?FFT-Multi-grained Mix Attention Transformerfferent scripts. Specifically, scene text-based script identification is challenged by inter-language similarities, complex backgrounds, and diverse text styles. To address the above problem, we use FFT Block to map the token to the frequency domain and decompose it into multiple frequency component作者: arbiter 時(shí)間: 2025-3-29 15:47
SAGHOG: Self-supervised Autoencoder for?Generating HOG Features for?Writer Retrievalg involves the application of the Segment Anything technique to extract handwriting from various datasets, ending up with about 24k documents, followed by training a vision transformer on reconstructing masked patches of the handwriting. . is then finetuned by appending NetRVLAD as an encoding layer作者: 全國(guó)性 時(shí)間: 2025-3-29 22:09
Analysis of?the?Calibration of?Handwriting Text Recognition Modelsable when facing new data. In this context, it is essential to correctly estimate an approximate error of the target predictions. To achieve this, the model must be well calibrated, meaning that the confidence values are sufficiently representative of the expected accuracy. Calibration is a crucial 作者: Fierce 時(shí)間: 2025-3-30 02:15 作者: mutineer 時(shí)間: 2025-3-30 07:52 作者: 裂口 時(shí)間: 2025-3-30 12:18
Reading Order Independent Metrics for?Information Extraction in?Handwritten Documents (NER) over such transcription. For?this reason, in publicly available datasets, the performance of?the systems is usually evaluated with metrics particular to?each dataset. Moreover, most of the metrics employed are sensitive?to reading order errors. Therefore, they do not reflect the expected fina作者: 包租車船 時(shí)間: 2025-3-30 13:59 作者: 真實(shí)的你 時(shí)間: 2025-3-30 18:24
Robust Handwritten Signature Representation with?Continual Learning of?Synthetic Data over?Predefinelts in deep learning require a significant amount of training data. The GPDS-960 dataset used to be the largest publicly available dataset of offline handwritten signatures for training deep models. However, due?to data protection regulatory issues, the GPDS-960 dataset is no longer publicly availab作者: 臭了生氣 時(shí)間: 2025-3-30 23:28
Deep Metric Learning with?Cross-Writer Attention for?Offline Signature Verification verification in?the writer-independent scenario remains a challenge, particularly?in distinguishing between genuine signatures and skilled forgeries.?In this paper, we propose a writer-independent signature verification method based on deep metric learning with cross-writer attention. Our cross-wri作者: agglomerate 時(shí)間: 2025-3-31 02:13
Content-Based Similarity for Automatic Scoring of Handwritten Descriptive Answersal expressions. Our experiments were made on a collection of handwritten descriptive answers from elementary school students, encompassing 37,500 Japanese, 15,896 English, and 86,264 math answers. We used neural network-based online and offline handwriting recognizers for each answer and applied aut作者: 竊喜 時(shí)間: 2025-3-31 07:03 作者: transdermal 時(shí)間: 2025-3-31 13:08
