存心
发表于 2025-3-28 16:52:36
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使乳化
发表于 2025-3-28 21:13:59
Paula Kasares,Ane Ortega,Estibaliz Amorrortuition at ICPR 2022. The data set includes 15 different chart categories, including 22,923 training images and 13,260 test images. We have implemented a vision-based transformer model that produces state-of-the-art results in chart classification.
collagen
发表于 2025-3-28 23:57:09
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contradict
发表于 2025-3-29 04:57:04
Karel van Hulle,Leo van der Tasents..The evaluations were carried out on the KABOOM-ONOMATOPOEIA dataset and show the relevance of our method in comparison with methods of the literature, which makes it a promising tool in the field of scene text detection.
出血
发表于 2025-3-29 09:38:02
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Trochlea
发表于 2025-3-29 15:11:03
Document Analysis and Recognition – ICDAR 2023 WorkshopsSan José, CA, USA, A
猛烈责骂
发表于 2025-3-29 15:52:02
Beyond Human Forgeries: An Investigation into Detecting Diffusion-Generated Handwritingn methods. Our experiments indicate that the strongest discriminative features do not come from generation artifacts, letter shapes, or the generative model’s architecture, but instead originate from real-world artifacts in genuine handwriting that are not reproduced by generative methods.
osteoclasts
发表于 2025-3-29 20:27:46
Leveraging Large Language Models for Topic Classification in the Domain of Public Affairstokens. Our experiments assess the performance of 4 different Spanish LLMs to classify up to 30 different topics in the data in different configurations. The results shows that LLMs can be of great use to process domain-specific documents, such as those in the domain of public affairs.
jarring
发表于 2025-3-30 00:22:57
Using GANs for Domain Adaptive High Resolution Synthetic Document Generationbased) represented a preliminary step towards the generation of realistic document images, it was impeded by its incapacity to produce high-resolution outputs. Our research aims to overcome this restriction, enhancing the DocSynth model’s capacity to generate high-resolution document images. Additio
Mercantile
发表于 2025-3-30 06:39:17
A Survey and Approach to Chart Classificationition at ICPR 2022. The data set includes 15 different chart categories, including 22,923 training images and 13,260 test images. We have implemented a vision-based transformer model that produces state-of-the-art results in chart classification.