Zobrazeno 1 - 10
of 483
pro vyhledávání: '"Martinez, Tony"'
Autor:
Archibald, Taylor, Martinez, Tony
Efficient categorization of historical documents is crucial for fields such as genealogy, legal research, and historical scholarship, where manual classification is impractical for large collections due to its labor-intensive and error-prone nature.
Externí odkaz:
http://arxiv.org/abs/2405.14162
Autor:
Archibald, Taylor, Martinez, Tony
Document semantic segmentation is a promising avenue that can facilitate document analysis tasks, including optical character recognition (OCR), form classification, and document editing. Although several synthetic datasets have been developed to dis
Externí odkaz:
http://arxiv.org/abs/2404.19259
Autor:
Lin, Fanqing, Martinez, Tony
Color-based two-hand 3D pose estimation in the global coordinate system is essential in many applications. However, there are very few datasets dedicated to this task and no existing dataset supports estimation in a non-laboratory environment. This i
Externí odkaz:
http://arxiv.org/abs/2206.04927
As deep neural networks become the state-of-the-art approach in the field of computer vision for dense prediction tasks, many methods have been developed for automatic estimation of the target outputs given the visual inputs. Although the estimation
Externí odkaz:
http://arxiv.org/abs/2112.10969
Stroke order and velocity are helpful features in the fields of signature verification, handwriting recognition, and handwriting synthesis. Recovering these features from offline handwritten text is a challenging and well-studied problem. We propose
Externí odkaz:
http://arxiv.org/abs/2105.11559
Hand segmentation and detection in truly unconstrained RGB-based settings is important for many applications. However, existing datasets are far from sufficient in terms of size and variety due to the infeasibility of manual annotation of large amoun
Externí odkaz:
http://arxiv.org/abs/2011.07252
We tackle the challenging task of estimating global 3D joint locations for both hands via only monocular RGB input images. We propose a novel multi-stage convolutional neural network based pipeline that accurately segments and locates the hands despi
Externí odkaz:
http://arxiv.org/abs/2006.01320
Autor:
Tensmeyer, Chris, Wigington, Curtis, Davis, Brian, Stewart, Seth, Martinez, Tony, Barrett, William
Training state-of-the-art offline handwriting recognition (HWR) models requires large labeled datasets, but unfortunately such datasets are not available in all languages and domains due to the high cost of manual labeling.We address this problem by
Externí odkaz:
http://arxiv.org/abs/1808.01423
Classifying pages or text lines into font categories aids transcription because single font Optical Character Recognition (OCR) is generally more accurate than omni-font OCR. We present a simple framework based on Convolutional Neural Networks (CNNs)
Externí odkaz:
http://arxiv.org/abs/1708.03669
Autor:
Tensmeyer, Chris, Martinez, Tony
Convolutional Neural Networks (CNNs) are state-of-the-art models for document image classification tasks. However, many of these approaches rely on parameters and architectures designed for classifying natural images, which differ from document image
Externí odkaz:
http://arxiv.org/abs/1708.03273