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Author |
Miquel Ferrer; F. Serratosa; Ernest Valveny |
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Title |
On the Relation Between the Median Graph and the Maximum Common Subgraph of a Set of Graphs |
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2007 |
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Alicante (Spain) |
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DAG |
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DAG @ dag @ FSV2007 |
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790 |
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Author |
Youssef El Rhabi; Simon Loic; Brun Luc |
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Title |
Estimation de la pose d’une caméra à partir d’un flux vidéo en s’approchant du temps réel |
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Conference Article |
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Year |
2015 |
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15ème édition d'ORASIS, journées francophones des jeunes chercheurs en vision par ordinateur ORASIS2015 |
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Augmented Reality; SFM; SLAM; real time pose computation; 2D/3D registration |
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Finding a way to estimate quickly and robustly the pose of an image is essential in augmented reality. Here we will discuss the approach we chose in order to get closer to real time by using SIFT points [4]. We propose a method based on filtering both SIFT points and images on which to focus on. Hence we will focus on relevant data. |
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Amiens; France; June 2015 |
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ORASIS |
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DAG; 600.077 |
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Admin @ si @ RLL2015 |
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2626 |
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Author |
Giacomo Magnifico; Beata Megyesi; Mohamed Ali Souibgui; Jialuo Chen; Alicia Fornes |
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Title |
Lost in Transcription of Graphic Signs in Ciphers |
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Conference Article |
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2022 |
Publication |
International Conference on Historical Cryptology (HistoCrypt 2022) |
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153-158 |
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transcription of ciphers; hand-written text recognition of symbols; graphic signs |
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Hand-written Text Recognition techniques with the aim to automatically identify and transcribe hand-written text have been applied to historical sources including ciphers. In this paper, we compare the performance of two machine learning architectures, an unsupervised method based on clustering and a deep learning method with few-shot learning. Both models are tested on seen and unseen data from historical ciphers with different symbol sets consisting of various types of graphic signs. We compare the models and highlight their differences in performance, with their advantages and shortcomings. |
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Amsterdam, Netherlands, June 20-22, 2022 |
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HystoCrypt |
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DAG; 600.121; 600.162; 602.230; 600.140 |
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Admin @ si @ MBS2022 |
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3731 |
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Author |
Hongxing Gao; Marçal Rusiñol; Dimosthenis Karatzas; Josep Llados |
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Title |
Fast Structural Matching for Document Image Retrieval through Spatial Databases |
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Conference Article |
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2014 |
Publication |
Document Recognition and Retrieval XXI |
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9021 |
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Document image retrieval; distance transform; MSER; spatial database |
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The structure of document images plays a signi cant role in document analysis thus considerable e orts have been made towards extracting and understanding document structure, usually in the form of layout analysis approaches. In this paper, we rst employ Distance Transform based MSER (DTMSER) to eciently extract stable document structural elements in terms of a dendrogram of key-regions. Then a fast structural matching method is proposed to query the structure of document (dendrogram) based on a spatial database which facilitates the formulation of advanced spatial queries. The experiments demonstrate a signi cant improvement in a document retrieval scenario when compared to the use of typical Bag of Words (BoW) and pyramidal BoW descriptors. |
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Amsterdam; September 2014 |
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SPIE-DRR |
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DAG; 600.056; 600.061; 600.077 |
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Admin @ si @ GRK2014a |
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2496 |
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Author |
Y. Patel; Lluis Gomez; Marçal Rusiñol; Dimosthenis Karatzas |
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Title |
Dynamic Lexicon Generation for Natural Scene Images |
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Conference Article |
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Year |
2016 |
Publication |
14th European Conference on Computer Vision Workshops |
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395-410 |
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Keywords |
scene text; photo OCR; scene understanding; lexicon generation; topic modeling; CNN |
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Many scene text understanding methods approach the endtoend recognition problem from a word-spotting perspective and take huge bene t from using small per-image lexicons. Such customized lexicons are normally assumed as given and their source is rarely discussed.
In this paper we propose a method that generates contextualized lexicons
for scene images using only visual information. For this, we exploit
the correlation between visual and textual information in a dataset consisting
of images and textual content associated with them. Using the topic modeling framework to discover a set of latent topics in such a dataset allows us to re-rank a xed dictionary in a way that prioritizes the words that are more likely to appear in a given image. Moreover, we train a CNN that is able to reproduce those word rankings but using only the image raw pixels as input. We demonstrate that the quality of the automatically obtained custom lexicons is superior to a generic frequency-based baseline. |
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Amsterdam; The Netherlands; October 2016 |
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ECCVW |
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Notes |
DAG; 600.084 |
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no |
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Admin @ si @ PGR2016 |
Serial |
2825 |
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Author |
Pau Riba; Josep Llados; Alicia Fornes |
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Title |
Error-tolerant coarse-to-fine matching model for hierarchical graphs |
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Conference Article |
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2017 |
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11th IAPR-TC-15 International Workshop on Graph-Based Representations in Pattern Recognition |
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10310 |
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107-117 |
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Graph matching; Hierarchical graph; Graph-based representation; Coarse-to-fine matching |
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Graph-based representations are effective tools to capture structural information from visual elements. However, retrieving a query graph from a large database of graphs implies a high computational complexity. Moreover, these representations are very sensitive to noise or small changes. In this work, a novel hierarchical graph representation is designed. Using graph clustering techniques adapted from graph-based social media analysis, we propose to generate a hierarchy able to deal with different levels of abstraction while keeping information about the topology. For the proposed representations, a coarse-to-fine matching method is defined. These approaches are validated using real scenarios such as classification of colour images and handwritten word spotting. |
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Anacapri; Italy; May 2017 |
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Springer International Publishing |
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Pasquale Foggia; Cheng-Lin Liu; Mario Vento |
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GbRPR |
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DAG; 600.097; 601.302; 600.121 |
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Admin @ si @ RLF2017a |
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2951 |
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Author |
Josep Llados; Ernest Valveny; Gemma Sanchez; Enric Marti |
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Title |
A Case Study of Pattern Recognition: Symbol Recognition in Graphic Documentsa |
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Conference Article |
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2003 |
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Proceedings of Pattern Recognition in Information Systems |
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1-13 |
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Angers, France |
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ICEIS Press |
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972-98816-3-4 |
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PRIS'03 |
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DAG;IAM; |
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IAM @ iam @ LVS2003 |
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1576 |
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Author |
David Aldavert |
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Title |
Efficient and Scalable Handwritten Word Spotting on Historical Documents using Bag of Visual Words |
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2021 |
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PhD Thesis, Universitat Autonoma de Barcelona-CVC |
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Word spotting can be defined as the pattern recognition tasked aimed at locating and retrieving a specific keyword within a document image collection without explicitly transcribing the whole corpus. Its use is particularly interesting when applied in scenarios where Optical Character Recognition performs poorly or can not be used at all. This thesis focuses on such a scenario, word spotting on historical handwritten documents that have been written by a single author or by multiple authors with a similar calligraphy.
This problem requires a visual signature that is robust to image artifacts, flexible to accommodate script variations and efficient to retrieve information in a rapid manner. For this, we have developed a set of word spotting methods that on their foundation use the well known Bag-of-Visual-Words (BoVW) representation. This representation has gained popularity among the document image analysis community to characterize handwritten words
in an unsupervised manner. However, most approaches on this field rely on a basic BoVW configuration and disregard complex encoding and spatial representations. We determine which BoVW configurations provide the best performance boost to a spotting system.
Then, we extend the segmentation-based word spotting, where word candidates are given a priori, to segmentation-free spotting. The proposed approach seeds the document images with overlapping word location candidates and characterizes them with a BoVW signature. Retrieval is achieved comparing the query and candidate signatures and returning the locations that provide a higher consensus. This is a simple but powerful approach that requires a more compact signature than in a segmentation-based scenario. We first
project the BoVW signature into a reduced semantic topics space and then compress it further using Product Quantizers. The resulting signature only requires a few dozen bytes, allowing us to index thousands of pages on a common desktop computer. The final system still yields a performance comparable to the state-of-the-art despite all the information loss during the compression phases.
Afterwards, we also study how to combine different modalities of information in order to create a query-by-X spotting system where, words are indexed using an information modality and queries are retrieved using another. We consider three different information modalities: visual, textual and audio. Our proposal is to create a latent feature space where features which are semantically related are projected onto the same topics. Creating thus a new feature space where information from different modalities can be compared. Later, we consider the codebook generation and descriptor encoding problem. The codebooks used to encode the BoVW signatures are usually created using an unsupervised clustering algorithm and, they require to test multiple parameters to determine which configuration is best for a certain document collection. We propose a semantic clustering algorithm which allows to estimate the best parameter from data. Since gather annotated data is costly, we use synthetically generated word images. The resulting codebook is database agnostic, i. e. a codebook that yields a good performance on document collections that use the same script. We also propose the use of an additional codebook to approximate descriptors and reduce the descriptor encoding
complexity to sub-linear.
Finally, we focus on the problem of signatures dimensionality. We propose a new symbol probability signature where each bin represents the probability that a certain symbol is present a certain location of the word image. This signature is extremely compact and combined with compression techniques can represent word images with just a few bytes per signature. |
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April 2021 |
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Ph.D. thesis |
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Publisher |
Ediciones Graficas Rey |
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Marçal Rusiñol;Josep Llados |
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978-84-122714-5-4 |
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DAG; 600.121;ADAS |
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Admin @ si @ Ald2021 |
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3601 |
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Author |
Lei Kang; Marçal Rusiñol; Alicia Fornes; Pau Riba; Mauricio Villegas |
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Title |
Unsupervised Adaptation for Synthetic-to-Real Handwritten Word Recognition |
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Conference Article |
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2020 |
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IEEE Winter Conference on Applications of Computer Vision |
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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 labelled data. To alleviate such problems, synthetic data generation and data augmentation are typically used to train HTR systems. However, training with such data produces encouraging but still inaccurate transcriptions in real words. In this paper, we propose an unsupervised writer adaptation approach that is able to automatically adjust a generic handwritten word recognizer, fully trained with synthetic fonts, towards a new incoming writer. We have experimentally validated our proposal using five different datasets, covering several challenges (i) the document source: modern and historic samples, which may involve paper degradation problems; (ii) different handwriting styles: single and multiple writer collections; and (iii) language, which involves different character combinations. Across these challenging collections, we show that our system is able to maintain its performance, thus, it provides a practical and generic approach to deal with new document collections without requiring any expensive and tedious manual annotation step. |
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Aspen; Colorado; USA; March 2020 |
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WACV |
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DAG; 600.129; 600.140; 601.302; 601.312; 600.121 |
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Admin @ si @ KRF2020 |
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3446 |
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Author |
Andres Mafla; Sounak Dey; Ali Furkan Biten; Lluis Gomez; Dimosthenis Karatzas |
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Title |
Fine-grained Image Classification and Retrieval by Combining Visual and Locally Pooled Textual Features |
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2020 |
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IEEE Winter Conference on Applications of Computer Vision |
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Text contained in an image carries high-level semantics that can be exploited to achieve richer image understanding. In particular, the mere presence of text provides strong guiding content that should be employed to tackle a diversity of computer vision tasks such as image retrieval, fine-grained classification, and visual question answering. In this paper, we address the problem of fine-grained classification and image retrieval by leveraging textual information along with visual cues to comprehend the existing intrinsic relation between the two modalities. The novelty of the proposed model consists of the usage of a PHOC descriptor to construct a bag of textual words along with a Fisher Vector Encoding that captures the morphology of text. This approach provides a stronger multimodal representation for this task and as our experiments demonstrate, it achieves state-of-the-art results on two different tasks, fine-grained classification and image retrieval. |
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Aspen; Colorado; USA; March 2020 |
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WACV |
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DAG; 600.121; 600.129 |
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Admin @ si @ MDB2020 |
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3334 |
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