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Author |
Lei Kang; Pau Riba; Marçal Rusiñol; Alicia Fornes; Mauricio Villegas |
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Title |
Distilling Content from Style for Handwritten Word Recognition |
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Conference Article |
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Year |
2020 |
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17th International Conference on Frontiers in Handwriting Recognition |
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Despite the latest transcription accuracies reached using deep neural network architectures, handwritten text recognition still remains a challenging problem, mainly because of the large inter-writer style variability. Both augmenting the training set with artificial samples using synthetic fonts, and writer adaptation techniques have been proposed to yield more generic approaches aimed at dodging style unevenness. In this work, we take a step closer to learn style independent features from handwritten word images. We propose a novel method that is able to disentangle the content and style aspects of input images by jointly optimizing a generative process and a handwritten
word recognizer. The generator is aimed at transferring writing style features from one sample to another in an image-to-image translation approach, thus leading to a learned content-centric features that shall be independent to writing style attributes.
Our proposed recognition model is able then to leverage such writer-agnostic features to reach better recognition performances. We advance over prior training strategies and demonstrate with qualitative and quantitative evaluations the performance of both
the generative process and the recognition efficiency in the IAM dataset. |
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Virtual ICFHR; September 2020 |
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ICFHR |
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DAG; 600.129; 600.140; 600.121 |
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no |
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Call Number |
Admin @ si @ KRR2020 |
Serial |
3425 |
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Author |
Raul Gomez; Jaume Gibert; Lluis Gomez; Dimosthenis Karatzas |
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Title |
Location Sensitive Image Retrieval and Tagging |
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Conference Article |
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Year |
2020 |
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16th European Conference on Computer Vision |
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People from different parts of the globe describe objects and concepts in distinct manners. Visual appearance can thus vary across different geographic locations, which makes location a relevant contextual information when analysing visual data. In this work, we address the task of image retrieval related to a given tag conditioned on a certain location on Earth. We present LocSens, a model that learns to rank triplets of images, tags and coordinates by plausibility, and two training strategies to balance the location influence in the final ranking. LocSens learns to fuse textual and location information of multimodal queries to retrieve related images at different levels of location granularity, and successfully utilizes location information to improve image tagging. |
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Virtual; August 2020 |
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ECCV |
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DAG; 600.121; 600.129 |
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Call Number |
Admin @ si @ GGG2020b |
Serial |
3420 |
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Author |
Lei Kang; Pau Riba; Yaxing Wang; Marçal Rusiñol; Alicia Fornes; Mauricio Villegas |
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Title |
GANwriting: Content-Conditioned Generation of Styled Handwritten Word Images |
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Conference Article |
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2020 |
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16th European Conference on Computer Vision |
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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 different writers, and even when analyzing words scribbled by the same individual, involuntary variations are conspicuous. In this work, we take a step closer to producing realistic and varied artificially rendered handwritten words. We propose a novel method that is able to produce credible handwritten word images by conditioning the generative process with both calligraphic style features and textual content. Our generator is guided by three complementary learning objectives: to produce realistic images, to imitate a certain handwriting style and to convey a specific textual content. Our model is unconstrained to any predefined vocabulary, being able to render whatever input word. Given a sample writer, it is also able to mimic its calligraphic features in a few-shot setup. We significantly advance over prior art and demonstrate with qualitative, quantitative and human-based evaluations the realistic aspect of our synthetically produced images. |
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Virtual; August 2020 |
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ECCV |
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DAG; 600.140; 600.121; 600.129 |
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Call Number |
Admin @ si @ KPW2020 |
Serial |
3426 |
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Author |
Mohamed Ali Souibgui; Y.Kessentini; Alicia Fornes |
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Title |
A conditional GAN based approach for distorted camera captured documents recovery |
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Conference Article |
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Year |
2020 |
Publication |
4th Mediterranean Conference on Pattern Recognition and Artificial Intelligence |
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Virtual; December 2020 |
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MedPRAI |
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DAG; 600.121 |
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Admin @ si @ SKF2020 |
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3450 |
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Author |
Mohamed Ali Souibgui; Alicia Fornes; Y.Kessentini; C.Tudor |
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Title |
A Few-shot Learning Approach for Historical Encoded Manuscript Recognition |
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Conference Article |
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Year |
2021 |
Publication |
25th International Conference on Pattern Recognition |
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5413-5420 |
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Encoded (or ciphered) manuscripts are a special type of historical documents that contain encrypted text. The automatic recognition of this kind of documents is challenging because: 1) the cipher alphabet changes from one document to another, 2) there is a lack of annotated corpus for training and 3) touching symbols make the symbol segmentation difficult and complex. To overcome these difficulties, we propose a novel method for handwritten ciphers recognition based on few-shot object detection. Our method first detects all symbols of a given alphabet in a line image, and then a decoding step maps the symbol similarity scores to the final sequence of transcribed symbols. By training on synthetic data, we show that the proposed architecture is able to recognize handwritten ciphers with unseen alphabets. In addition, if few labeled pages with the same alphabet are used for fine tuning, our method surpasses existing unsupervised and supervised HTR methods for ciphers recognition. |
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Virtual; January 2021 |
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ICPR |
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DAG; 600.121; 600.140 |
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Admin @ si @ SFK2021 |
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3449 |
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Author |
Andres Mafla; Sounak Dey; Ali Furkan Biten; Lluis Gomez; Dimosthenis Karatzas |
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Title |
Multi-modal reasoning graph for scene-text based fine-grained image classification and retrieval |
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2021 |
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IEEE Winter Conference on Applications of Computer Vision |
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4022-4032 |
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Virtual; January 2021 |
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WACV |
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DAG; 600.121 |
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Admin @ si @ MDB2021 |
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3491 |
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Author |
Andres Mafla; Rafael S. Rezende; Lluis Gomez; Diana Larlus; Dimosthenis Karatzas |
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Title |
StacMR: Scene-Text Aware Cross-Modal Retrieval |
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Conference Article |
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2021 |
Publication |
IEEE Winter Conference on Applications of Computer Vision |
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2219-2229 |
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Virtual; January 2021 |
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WACV |
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DAG; 600.121 |
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Admin @ si @ MRG2021a |
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3492 |
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Author |
Minesh Mathew; Dimosthenis Karatzas; C.V. Jawahar |
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Title |
DocVQA: A Dataset for VQA on Document Images |
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Conference Article |
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2021 |
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IEEE Winter Conference on Applications of Computer Vision |
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2200-2209 |
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We present a new dataset for Visual Question Answering (VQA) on document images called DocVQA. The dataset consists of 50,000 questions defined on 12,000+ document images. Detailed analysis of the dataset in comparison with similar datasets for VQA and reading comprehension is presented. We report several baseline results by adopting existing VQA and reading comprehension models. Although the existing models perform reasonably well on certain types of questions, there is large performance gap compared to human performance (94.36% accuracy). The models need to improve specifically on questions where understanding structure of the document is crucial. The dataset, code and leaderboard are available at docvqa. org |
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Virtual; January 2021 |
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WACV |
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DAG; 600.121 |
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Admin @ si @ MKJ2021 |
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3498 |
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Author |
Asma Bensalah; Jialuo Chen; Alicia Fornes; Cristina Carmona_Duarte; Josep Llados; Miguel A. Ferrer |
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Title |
Towards Stroke Patients' Upper-limb Automatic Motor Assessment Using Smartwatches. |
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Conference Article |
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2020 |
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International Workshop on Artificial Intelligence for Healthcare Applications |
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12661 |
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476-489 |
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Assessing the physical condition in rehabilitation scenarios is a challenging problem, since it involves Human Activity Recognition (HAR) and kinematic analysis methods. In addition, the difficulties increase in unconstrained rehabilitation scenarios, which are much closer to the real use cases. In particular, our aim is to design an upper-limb assessment pipeline for stroke patients using smartwatches. We focus on the HAR task, as it is the first part of the assessing pipeline. Our main target is to automatically detect and recognize four key movements inspired by the Fugl-Meyer assessment scale, which are performed in both constrained and unconstrained scenarios. In addition to the application protocol and dataset, we propose two detection and classification baseline methods. We believe that the proposed framework, dataset and baseline results will serve to foster this research field. |
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Virtual; January 2021 |
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ICPRW |
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DAG; 600.121; 600.140; |
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Call Number |
Admin @ si @ BCF2020 |
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3508 |
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Author |
Manuel Carbonell; Pau Riba; Mauricio Villegas; Alicia Fornes; Josep Llados |
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Title |
Named Entity Recognition and Relation Extraction with Graph Neural Networks in Semi Structured Documents |
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Conference Article |
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Year |
2020 |
Publication |
25th International Conference on Pattern Recognition |
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The use of administrative documents to communicate and leave record of business information requires of methods
able to automatically extract and understand the content from
such documents in a robust and efficient way. In addition,
the semi-structured nature of these reports is specially suited
for the use of graph-based representations which are flexible
enough to adapt to the deformations from the different document
templates. Moreover, Graph Neural Networks provide the proper
methodology to learn relations among the data elements in
these documents. In this work we study the use of Graph
Neural Network architectures to tackle the problem of entity
recognition and relation extraction in semi-structured documents.
Our approach achieves state of the art results in the three
tasks involved in the process. Additionally, the experimentation
with two datasets of different nature demonstrates the good
generalization ability of our approach. |
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Virtual; January 2021 |
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ICPR |
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DAG; 600.121 |
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Call Number |
Admin @ si @ CRV2020 |
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3509 |
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