64 citations · 149 across the 12 of their papers we have counts for
14 papers
EAML: Ensemble Self-Attention-based Mutual Learning Network for Document Image Classification
Souhail Bakkali, Ziheng Ming, Mickael Coustaty +1
In the recent past, complex deep neural networks have received huge interest in various document understanding tasks such as document image classification and document retrieval. A…
Content and Style Aware Generation of Text-line Images for Handwriting Recognition
Lei Kang, Pau Riba, Marçal Rusiñol +2
Handwritten Text Recognition has achieved an impressive performance in public benchmarks. However, due to the high inter- and intra-class variability between handwriting styles, su…
RoadText-1K: Text Detection & Recognition Dataset for Driving Videos
Sangeeth Reddy, Minesh Mathew, Lluis Gomez +3
Perceiving text is crucial to understand semantics of outdoor scenes and hence is a critical requirement to build intelligent systems for driver assistance and self-driving. Most o…
GANwriting: Content-Conditioned Generation of Styled Handwritten Word Images
Lei Kang, Pau Riba, Yaxing Wang +3
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 contrar…
Candidate Fusion: Integrating Language Modelling into a Sequence-to-Sequence Handwritten Word Recognition Architecture
Lei Kang, Pau Riba, Mauricio Villegas +2
Sequence-to-sequence models have recently become very popular for tackling handwritten word recognition problems. However, how to effectively integrate an external language model i…
Unsupervised Adaptation for Synthetic-to-Real Handwritten Word Recognition
Lei Kang, Marçal Rusiñol, Alicia Fornés +2
Handwritten Text Recognition (HTR) is still a challenging problem because it must deal with two important difficulties: the variability among writing styles, and the scarcity of la…
ICDAR 2019 Competition on Scene Text Visual Question Answering
Ali Furkan Biten, Rubèn Tito, Andres Mafla +6
This paper presents final results of ICDAR 2019 Scene Text Visual Question Answering competition (ST-VQA). ST-VQA introduces an important aspect that is not addressed by any Visual…
Scene Text Visual Question Answering
Ali Furkan Biten, Ruben Tito, Andres Mafla +5
Current visual question answering datasets do not consider the rich semantic information conveyed by text within an image. In this work, we present a new dataset, ST-VQA, that aims…
Good News, Everyone! Context driven entity-aware captioning for news images
Ali Furkan Biten, Lluis Gomez, Marçal Rusiñol +1
Current image captioning systems perform at a merely descriptive level, essentially enumerating the objects in the scene and their relations. Humans, on the contrary, interpret ima…
Self-Supervised Visual Representations for Cross-Modal Retrieval
Yash Patel, Lluis Gomez, Marçal Rusiñol +2
Cross-modal retrieval methods have been significantly improved in last years with the use of deep neural networks and large-scale annotated datasets such as ImageNet and Places. Ho…
Single Shot Scene Text Retrieval
Lluís Gómez, Andrés Mafla, Marçal Rusiñol +1
Textual information found in scene images provides high level semantic information about the image and its context and it can be leveraged for better scene understanding. In this p…
TextTopicNet - Self-Supervised Learning of Visual Features Through Embedding Images on Semantic Text Spaces
Yash Patel, Lluis Gomez, Raul Gomez +3
The immense success of deep learning based methods in computer vision heavily relies on large scale training datasets. These richly annotated datasets help the network learn discri…
The Robust Reading Competition Annotation and Evaluation Platform
Dimosthenis Karatzas, Lluis Gómez, Anguelos Nicolaou +1
The ICDAR Robust Reading Competition (RRC), initiated in 2003 and re-established in 2011, has become a de-facto evaluation standard for robust reading systems and algorithms. Concu…
Self-supervised learning of visual features through embedding images into text topic spaces
Lluis Gomez, Yash Patel, Marçal Rusiñol +2
End-to-end training from scratch of current deep architectures for new computer vision problems would require Imagenet-scale datasets, and this is not always possible. In this pape…