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Author |
Ernest Valveny; Philippe Dosch; Alicia Fornes |
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Title  |
Report on the Third Contest on Symbol Recognition |
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Book Chapter |
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Year |
2008 |
Publication |
Graphics Recognition: Recent Advances and New Opportunities |
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5046 |
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321–328 |
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W. Liu, J. Llados, J.M. Ogier |
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DAG |
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DAG @ dag @ VDF2008 |
Serial |
986 |
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Author |
P. Wang; V. Eglin; C. Garcia; C. Largeron; Josep Llados; Alicia Fornes |
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Title |
Représentation par graphe de mots manuscrits dans les images pour la recherche par similarité |
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Conference Article |
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Year |
2014 |
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Colloque International Francophone sur l'Écrit et le Document |
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233-248 |
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Keywords |
word spotting; graph-based representation; shape context description; graph edit distance; DTW; block merging; query by example |
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Abstract |
Effective information retrieval on handwritten document images has always been
a challenging task. In this paper, we propose a novel handwritten word spotting approach based on graph representation. The presented model comprises both topological and morphological signatures of handwriting. Skeleton-based graphs with the Shape Context labeled vertexes are established for connected components. Each word image is represented as a sequence of graphs. In order to be robust to the handwriting variations, an exhaustive merging process based on DTW alignment results introduced in the similarity measure between word images. With respect to the computation complexity, an approximate graph edit distance approach using bipartite matching is employed for graph matching. The experiments on the George Washington dataset and the marriage records from the Barcelona Cathedral dataset demonstrate that the proposed approach outperforms the state-of-the-art structural methods. |
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Nancy; Francia; March 2014 |
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CIFED |
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DAG; 600.061; 602.006; 600.077 |
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Admin @ si @ WEG2014c |
Serial |
2564 |
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Author |
Joan Mas; J.A. Jorge; Gemma Sanchez; Josep Llados |
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Title |
Representing and Parsing Sketched Symbols using Adjacency Grammars and a Grid-Directed Parser |
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Book Chapter |
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Year |
2008 |
Publication |
Graphics Recognition: Recent Advances and New Opportunities, |
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5046 |
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176–187 |
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W. Liu, J. Llados, J.M. Ogier |
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DAG |
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DAG @ dag @ MJS2008 |
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991 |
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Author |
Raul Gomez; Yahui Liu; Marco de Nadai; Dimosthenis Karatzas; Bruno Lepri; Nicu Sebe |
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Title |
Retrieval Guided Unsupervised Multi-domain Image to Image Translation |
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Conference Article |
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Year |
2020 |
Publication |
28th ACM International Conference on Multimedia |
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Image to image translation aims to learn a mapping that transforms an image from one visual domain to another. Recent works assume that images descriptors can be disentangled into a domain-invariant content representation and a domain-specific style representation. Thus, translation models seek to preserve the content of source images while changing the style to a target visual domain. However, synthesizing new images is extremely challenging especially in multi-domain translations, as the network has to compose content and style to generate reliable and diverse images in multiple domains. In this paper we propose the use of an image retrieval system to assist the image-to-image translation task. First, we train an image-to-image translation model to map images to multiple domains. Then, we train an image retrieval model using real and generated images to find images similar to a query one in content but in a different domain. Finally, we exploit the image retrieval system to fine-tune the image-to-image translation model and generate higher quality images. Our experiments show the effectiveness of the proposed solution and highlight the contribution of the retrieval network, which can benefit from additional unlabeled data and help image-to-image translation models in the presence of scarce data. |
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ACM |
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DAG; 600.121 |
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Admin @ si @ GLN2020 |
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3497 |
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Author |
Sangeeth Reddy; Minesh Mathew; Lluis Gomez; Marçal Rusiñol; Dimosthenis Karatzas; C.V. Jawahar |
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Title |
RoadText-1K: Text Detection and Recognition Dataset for Driving Videos |
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Conference Article |
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2020 |
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IEEE International Conference on Robotics and Automation |
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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 of the existing datasets for text detection and recognition comprise still images and are mostly compiled keeping text in mind. This paper introduces a new ”RoadText-1K” dataset for text in driving videos. The dataset is 20 times larger than the existing largest dataset for text in videos. Our dataset comprises 1000 video clips of driving without any bias towards text and with annotations for text bounding boxes and transcriptions in every frame. State of the art methods for text detection,
recognition and tracking are evaluated on the new dataset and the results signify the challenges in unconstrained driving videos compared to existing datasets. This suggests that RoadText-1K is suited for research and development of reading systems, robust enough to be incorporated into more complex downstream tasks like driver assistance and self-driving. The dataset can be found at http://cvit.iiit.ac.in/research/
projects/cvit-projects/roadtext-1k |
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Paris; Francia; ??? |
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ICRA |
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Notes |
DAG; 600.121; 600.129 |
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no |
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Call Number |
Admin @ si @ RMG2020 |
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3400 |
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Author |
Lei Kang |
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Title |
Robust Handwritten Text Recognition in Scarce Labeling Scenarios: Disentanglement, Adaptation and Generation |
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Book Whole |
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Year |
2020 |
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PhD Thesis, Universitat Autonoma de Barcelona-CVC |
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Handwritten documents are not only preserved in historical archives but also widely used in administrative documents such as cheques and claims. With the rise of the deep learning era, many state-of-the-art approaches have achieved good performance on specific datasets for Handwritten Text Recognition (HTR). However, it is still challenging to solve real use cases because of the varied handwriting styles across different writers and the limited labeled data. Thus, both explorin a more robust handwriting recognition architectures and proposing methods to diminish the gap between the source and target data in an unsupervised way are
demanded.
In this thesis, firstly, we explore novel architectures for HTR, from Sequence-to-Sequence (Seq2Seq) method with attention mechanism to non-recurrent Transformer-based method. Secondly, we focus on diminishing the performance gap between source and target data in an unsupervised way. Finally, we propose a group of generative methods for handwritten text images, which could be utilized to increase the training set to obtain a more robust recognizer. In addition, by simply modifying the generative method and joining it with a recognizer, we end up with an effective disentanglement method to distill textual content from handwriting styles so as to achieve a generalized recognition performance.
We outperform state-of-the-art HTR performances in the experimental results among different scientific and industrial datasets, which prove the effectiveness of the proposed methods. To the best of our knowledge, the non-recurrent recognizer and the disentanglement method are the first contributions in the handwriting recognition field. Furthermore, we have outlined the potential research lines, which would be interesting to explore in the future. |
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Ph.D. thesis |
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Publisher |
Ediciones Graficas Rey |
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Editor |
Alicia Fornes;Marçal Rusiñol;Mauricio Villegas |
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978-84-122714-0-9 |
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DAG; 600.121 |
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Admin @ si @ Kan20 |
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3482 |
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Author |
Alicia Fornes; Josep Llados; Gemma Sanchez; Dimosthenis Karatzas |
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Title |
Rotation Invariant Hand-Drawn Symbol Recognition based on a Dynamic Time Warping Model |
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Journal Article |
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Year |
2010 |
Publication |
International Journal on Document Analysis and Recognition |
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IJDAR |
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Volume |
13 |
Issue |
3 |
Pages |
229–241 |
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One of the major difficulties of handwriting symbol recognition is the high variability among symbols because of the different writer styles. In this paper, we introduce a robust approach for describing and recognizing hand-drawn symbols tolerant to these writer style differences. This method, which is invariant to scale and rotation, is based on the dynamic time warping (DTW) algorithm. The symbols are described by vector sequences, a variation of the DTW distance is used for computing the matching distance, and K-Nearest Neighbor is used to classify them. Our approach has been evaluated in two benchmarking scenarios consisting of hand-drawn symbols. Compared with state-of-the-art methods for symbol recognition, our method shows higher tolerance to the irregular deformations induced by hand-drawn strokes. |
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Springer-Verlag |
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1433-2833 |
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DAG; IF 2009: 1,213 |
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DAG @ dag @ FLS2010a |
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1288 |
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Author |
Sangheeta Roy; Palaiahnakote Shivakumara; Namita Jain; Vijeta Khare; Anjan Dutta; Umapada Pal; Tong Lu |
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Title |
Rough-Fuzzy based Scene Categorization for Text Detection and Recognition in Video |
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Journal Article |
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2018 |
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Pattern Recognition |
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PR |
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80 |
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64-82 |
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Rough set; Fuzzy set; Video categorization; Scene image classification; Video text detection; Video text recognition |
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Scene image or video understanding is a challenging task especially when number of video types increases drastically with high variations in background and foreground. This paper proposes a new method for categorizing scene videos into different classes, namely, Animation, Outlet, Sports, e-Learning, Medical, Weather, Defense, Economics, Animal Planet and Technology, for the performance improvement of text detection and recognition, which is an effective approach for scene image or video understanding. For this purpose, at first, we present a new combination of rough and fuzzy concept to study irregular shapes of edge components in input scene videos, which helps to classify edge components into several groups. Next, the proposed method explores gradient direction information of each pixel in each edge component group to extract stroke based features by dividing each group into several intra and inter planes. We further extract correlation and covariance features to encode semantic features located inside planes or between planes. Features of intra and inter planes of groups are then concatenated to get a feature matrix. Finally, the feature matrix is verified with temporal frames and fed to a neural network for categorization. Experimental results show that the proposed method outperforms the existing state-of-the-art methods, at the same time, the performances of text detection and recognition methods are also improved significantly due to categorization. |
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DAG; 600.097; 600.121 |
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Admin @ si @ RSJ2018 |
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3096 |
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Author |
Lluis Pere de las Heras; David Fernandez; Alicia Fornes; Ernest Valveny; Gemma Sanchez; Josep Llados |
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Title |
Runlength Histogram Image Signature for Perceptual Retrieval of Architectural Floor Plans |
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Book Chapter |
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Year |
2014 |
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Graphics Recognition. Current Trends and Challenges |
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8746 |
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135-146 |
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Graphics recognition; Graphics retrieval; Image classification |
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This paper proposes a runlength histogram signature as a perceptual descriptor of architectural plans in a retrieval scenario. The style of an architectural drawing is characterized by the perception of lines, shapes and texture. Such visual stimuli are the basis for defining semantic concepts as space properties, symmetry, density, etc. We propose runlength histograms extracted in vertical, horizontal and diagonal directions as a characterization of line and space properties in floorplans, so it can be roughly associated to a description of walls and room structure. A retrieval application illustrates the performance of the proposed approach, where given a plan as a query, similar ones are obtained from a database. A ground truth based on human observation has been constructed to validate the hypothesis. Additional retrieval results on sketched building’s facades are reported qualitatively in this paper. Its good description and its adaptability to two different sketch drawings despite its simplicity shows the interest of the proposed approach and opens a challenging research line in graphics recognition. |
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Springer Berlin Heidelberg |
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0302-9743 |
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978-3-662-44853-3 |
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DAG; ADAS; 600.045; 600.056; 600.061; 600.076; 600.077 |
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Admin @ si @ HFF2014 |
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2536 |
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Author |
Lluis Pere de las Heras; David Fernandez; Alicia Fornes; Ernest Valveny; Gemma Sanchez; Josep Llados |
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Runlength Histogram Image Signature for Perceptual Retrieval of Architectural Floor Plans |
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2013 |
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10th IAPR International Workshop on Graphics Recognition |
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Bethlehem; PA; USA; August 2013 |
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GREC |
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DAG; 600.045; 600.061; 600.056 |
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Admin @ si @ HFF2013b |
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2695 |
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