Zobrazeno 1 - 10
of 66
pro vyhledávání: '"Riba, Pau"'
Handwritten Text Recognition has achieved an impressive performance in public benchmarks. However, due to the high inter- and intra-class variability between handwriting styles, such recognizers need to be trained using huge volumes of manually label
Externí odkaz:
http://arxiv.org/abs/2204.05539
This work investigates the problem of sketch-guided object localization (SGOL), where human sketches are used as queries to conduct the object localization in natural images. In this cross-modal setting, we first contribute with a tough-to-beat basel
Externí odkaz:
http://arxiv.org/abs/2109.11874
One of the major prerequisites for any deep learning approach is the availability of large-scale training data. When dealing with scanned document images in real world scenarios, the principal information of its content is stored in the layout itself
Externí odkaz:
http://arxiv.org/abs/2107.04357
Despite significant progress on current state-of-the-art image generation models, synthesis of document images containing multiple and complex object layouts is a challenging task. This paper presents a novel approach, called DocSynth, to automatical
Externí odkaz:
http://arxiv.org/abs/2107.02638
This paper presents a novel method for date estimation of historical photographs from archival sources. The main contribution is to formulate the date estimation as a retrieval task, where given a query, the retrieved images are ranked in terms of th
Externí odkaz:
http://arxiv.org/abs/2106.05618
In this paper, we explore and evaluate the use of ranking-based objective functions for learning simultaneously a word string and a word image encoder. We consider retrieval frameworks in which the user expects a retrieval list ranked according to a
Externí odkaz:
http://arxiv.org/abs/2106.05144
The emergence of geometric deep learning as a novel framework to deal with graph-based representations has faded away traditional approaches in favor of completely new methodologies. In this paper, we propose a new framework able to combine the advan
Externí odkaz:
http://arxiv.org/abs/2008.07641
The advent of recurrent neural networks for handwriting recognition marked an important milestone reaching impressive recognition accuracies despite the great variability that we observe across different writing styles. Sequential architectures are a
Externí odkaz:
http://arxiv.org/abs/2005.13044
Although current image generation methods have reached impressive quality levels, they are still unable to produce plausible yet diverse images of handwritten words. On the contrary, when writing by hand, a great variability is observed across differ
Externí odkaz:
http://arxiv.org/abs/2003.02567
Sequence-to-sequence models have recently become very popular for tackling handwritten word recognition problems. However, how to effectively integrate an external language model into such recognizer is still a challenging problem. The main challenge
Externí odkaz:
http://arxiv.org/abs/1912.10308