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Joana Maria Pujadas-Mora; Alicia Fornes; Oriol Ramos Terrades; Josep Llados; Jialuo Chen; Miquel Valls-Figols; Anna Cabre |

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Title |
The Barcelona Historical Marriage Database and the Baix Llobregat Demographic Database. From Algorithms for Handwriting Recognition to Individual-Level Demographic and Socioeconomic Data |
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Journal |
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2022 |
Publication |
Historical Life Course Studies |
Abbreviated Journal |
HLCS |
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12 |
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99-132 |
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Individual demographic databases; Computer vision, Record linkage; Social mobility; Inequality; Migration; Word spotting; Handwriting recognition; Local censuses; Marriage Licences |
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The Barcelona Historical Marriage Database (BHMD) gathers records of the more than 600,000 marriages celebrated in the Diocese of Barcelona and their taxation registered in Barcelona Cathedral's so-called Marriage Licenses Books for the long period 1451–1905 and the BALL Demographic Database brings together the individual information recorded in the population registers, censuses and fiscal censuses of the main municipalities of the county of Baix Llobregat (Barcelona). In this ongoing collection 263,786 individual observations have been assembled, dating from the period between 1828 and 1965 by December 2020. The two databases started as part of different interdisciplinary research projects at the crossroads of Historical Demography and Computer Vision. Their construction uses artificial intelligence and computer vision methods as Handwriting Recognition to reduce the time of execution. However, its current state still requires some human intervention which explains the implemented crowdsourcing and game sourcing experiences. Moreover, knowledge graph techniques have allowed the application of advanced record linkage to link the same individuals and families across time and space. Moreover, we will discuss the main research lines using both databases developed so far in historical demography. |
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June 23, 2022 |
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DAG; 600.121; 600.162; 602.230; 600.140 |
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Admin @ si @ PFR2022 |
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3737 |
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Stepan Simsa; Michal Uricar; Milan Sulc; Yash Patel; Ahmed Hamdi; Matej Kocian; Matyas Skalicky; Jiri Matas; Antoine Doucet; Mickael Coustaty; Dimosthenis Karatzas |


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Title |
Overview of DocILE 2023: Document Information Localization and Extraction |
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Conference Article |
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2023 |
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International Conference of the Cross-Language Evaluation Forum for European Languages |
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14163 |
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276–293 |
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Information Extraction; Computer Vision; Natural Language Processing; Optical Character Recognition; Document Understanding |
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This paper provides an overview of the DocILE 2023 Competition, its tasks, participant submissions, the competition results and possible future research directions. This first edition of the competition focused on two Information Extraction tasks, Key Information Localization and Extraction (KILE) and Line Item Recognition (LIR). Both of these tasks require detection of pre-defined categories of information in business documents. The second task additionally requires correctly grouping the information into tuples, capturing the structure laid out in the document. The competition used the recently published DocILE dataset and benchmark that stays open to new submissions. The diversity of the participant solutions indicates the potential of the dataset as the submissions included pure Computer Vision, pure Natural Language Processing, as well as multi-modal solutions and utilized all of the parts of the dataset, including the annotated, synthetic and unlabeled subsets. |
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Thessaloniki; Greece; September 2023 |
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CLEF |
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DAG |
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Admin @ si @ SUS2023a |
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3924 |
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V. Poulain d'Andecy; Emmanuel Hartmann; Marçal Rusiñol |


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Title |
Field Extraction by hybrid incremental and a-priori structural templates |
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2018 |
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13th IAPR International Workshop on Document Analysis Systems |
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251 - 256 |
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Layout Analysis; information extraction; incremental learning |
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In this paper, we present an incremental framework for extracting information fields from administrative documents. First, we demonstrate some limits of the existing state-of-the-art methods such as the delay of the system efficiency. This is a concern in industrial context when we have only few samples of each document class. Based on this analysis, we propose a hybrid system combining incremental learning by means of itf-df statistics and a-priori generic
models. We report in the experimental section our results obtained with a dataset of real invoices. |
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Viena; Austria; April 2018 |
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DAG; 600.084; 600.129; 600.121 |
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Admin @ si @ PHR2018 |
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3106 |
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Author |
Josep Llados; Jaime Lopez-Krahe; Enric Marti |


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A system to understand hand-drawn floor plans using subgraph isomorphism and Hough transform |
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1997 |
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Machine Vision and Applications |
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10 |
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3 |
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150-158 |
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Line drawings – Hough transform – Graph matching – CAD systems – Graphics recognition |
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Presently, man-machine interface development is a widespread research activity. A system to understand hand drawn architectural drawings in a CAD environment is presented in this paper. To understand a document, we have to identify its building elements and their structural properties. An attributed graph structure is chosen as a symbolic representation of the input document and the patterns to recognize in it. An inexact subgraph isomorphism procedure using relaxation labeling techniques is performed. In this paper we focus on how to speed up the matching. There is a building element, the walls, characterized by a hatching pattern. Using a straight line Hough transform (SLHT)-based method, we recognize this pattern, characterized by parallel straight lines, and remove from the input graph the edges belonging to this pattern. The isomorphism is then applied to the remainder of the input graph. When all the building elements have been recognized, the document is redrawn, correcting the inaccurate strokes obtained from a hand-drawn input. |
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DAG;IAM |
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IAM @ iam @ LLM1997a |
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1566 |
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Author |
Sounak Dey; Anguelos Nicolaou; Josep Llados; Umapada Pal |


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Title |
Local Binary Pattern for Word Spotting in Handwritten Historical Document |
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Conference Article |
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2016 |
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Joint IAPR International Workshops on Statistical Techniques in Pattern Recognition (SPR) and Structural and Syntactic Pattern Recognition (SSPR) |
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574-583 |
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Local binary patterns; Spatial sampling; Learning-free; Word spotting; Handwritten; Historical document analysis; Large-scale data |
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Digital libraries store images which can be highly degraded and to index this kind of images we resort to word spotting as our information retrieval system. Information retrieval for handwritten document images is more challenging due to the difficulties in complex layout analysis, large variations of writing styles, and degradation or low quality of historical manuscripts. This paper presents a simple innovative learning-free method for word spotting from large scale historical documents combining Local Binary Pattern (LBP) and spatial sampling. This method offers three advantages: firstly, it operates in completely learning free paradigm which is very different from unsupervised learning methods, secondly, the computational time is significantly low because of the LBP features, which are very fast to compute, and thirdly, the method can be used in scenarios where annotations are not available. Finally, we compare the results of our proposed retrieval method with other methods in the literature and we obtain the best results in the learning free paradigm. |
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Merida; Mexico; December 2016 |
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S+SSPR |
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DAG; 600.097; 602.006; 603.053 |
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Admin @ si @ DNL2016 |
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2876 |
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Author |
David Fernandez; Pau Riba; Alicia Fornes; Josep Llados |


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Title |
On the Influence of Key Point Encoding for Handwritten Word Spotting |
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Conference Article |
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2014 |
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14th International Conference on Frontiers in Handwriting Recognition |
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476 - 481 |
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Local descriptors; Interest points; Handwritten documents; Word spotting; Historical document analysis |
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In this paper we evaluate the influence of the selection of key points and the associated features in the performance of word spotting processes. In general, features can be extracted from a number of characteristic points like corners, contours, skeletons, maxima, minima, crossings, etc. A number of descriptors exist in the literature using different interest point detectors. But the intrinsic variability of handwriting vary strongly on the performance if the interest points are not stable enough. In this paper, we analyze the performance of different descriptors for local interest points. As benchmarking dataset we have used the Barcelona Marriage Database that contains handwritten records of marriages over five centuries. |
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Creete Island; Grecia; September 2014 |
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2167-6445 |
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978-1-4799-4335-7 |
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ICFHR |
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DAG; 600.056; 600.061; 602.006; 600.077 |
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Admin @ si @ FRF2014 |
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2460 |
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Author |
Marçal Rusiñol; J. Chazalon; Jean-Marc Ogier |

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Title |
Filtrage de descripteurs locaux pour l'amélioration de la détection de documents |
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Conference Article |
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2016 |
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Colloque International Francophone sur l'Écrit et le Document |
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Local descriptors; mobile capture; document matching; keypoint selection |
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In this paper we propose an effective method aimed at reducing the amount of local descriptors to be indexed in a document matching framework.In an off-line training stage, the matching between the model document and incoming images is computed retaining the local descriptors from the model that steadily produce good matches. We have evaluated this approach by using the ICDAR2015 SmartDOC dataset containing near 25000 images from documents to be captured by a mobile device. We have tested the performance of this filtering step by using ORB and SIFT local detectors and descriptors. The results show an important gain both in quality of the final matching as well as in time and space requirements. |
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Toulouse; France; March 2016 |
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CIFED |
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DAG; 600.084; 600.077 |
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Admin @ si @ RCO2016 |
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2755 |
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A.Kesidis; Dimosthenis Karatzas |


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Title |
Logo and Trademark Recognition |
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2014 |
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Handbook of Document Image Processing and Recognition |
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D |
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591-646 |
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Logo recognition; Logo removal; Logo spotting; Trademark registration; Trademark retrieval systems |
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The importance of logos and trademarks in nowadays society is indisputable, variably seen under a positive light as a valuable service for consumers or a negative one as a catalyst of ever-increasing consumerism. This chapter discusses the technical approaches for enabling machines to work with logos, looking into the latest methodologies for logo detection, localization, representation, recognition, retrieval, and spotting in a variety of media. This analysis is presented in the context of three different applications covering the complete depth and breadth of state of the art techniques. These are trademark retrieval systems, logo recognition in document images, and logo detection and removal in images and videos. This chapter, due to the very nature of logos and trademarks, brings together various facets of document image analysis spanning graphical and textual content, while it links document image analysis to other computer vision domains, especially when it comes to the analysis of real-scene videos and images. |
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Springer London |
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D. Doermann; K. Tombre |
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978-0-85729-858-4 |
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DAG; 600.077 |
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Admin @ si @ KeK2014 |
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2425 |
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Carlos Boned Riera; Oriol Ramos Terrades |

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Title |
Discriminative Neural Variational Model for Unbalanced Classification Tasks in Knowledge Graph |
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Conference Article |
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2022 |
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26th International Conference on Pattern Recognition |
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2186-2191 |
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Measurement; Couplings; Semantics; Ear; Benchmark testing; Data models; Pattern recognition |
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Nowadays the paradigm of link discovery problems has shown significant improvements on Knowledge Graphs. However, method performances are harmed by the unbalanced nature of this classification problem, since many methods are easily biased to not find proper links. In this paper we present a discriminative neural variational auto-encoder model, called DNVAE from now on, in which we have introduced latent variables to serve as embedding vectors. As a result, the learnt generative model approximate better the underlying distribution and, at the same time, it better differentiate the type of relations in the knowledge graph. We have evaluated this approach on benchmark knowledge graph and Census records. Results in this last data set are quite impressive since we reach the highest possible score in the evaluation metrics. However, further experiments are still needed to deeper evaluate the performance of the method in more challenging tasks. |
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Montreal; Quebec; Canada; August 2022 |
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DAG; 600.121; 600.162 |
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Admin @ si @ BoR2022 |
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3741 |
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Author |
Ali Furkan Biten; Andres Mafla; Lluis Gomez; Dimosthenis Karatzas |


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Title |
Is An Image Worth Five Sentences? A New Look into Semantics for Image-Text Matching |
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Conference Article |
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2022 |
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Winter Conference on Applications of Computer Vision |
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1391-1400 |
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Measurement; Training; Integrated circuits; Annotations; Semantics; Training data; Semisupervised learning |
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The task of image-text matching aims to map representations from different modalities into a common joint visual-textual embedding. However, the most widely used datasets for this task, MSCOCO and Flickr30K, are actually image captioning datasets that offer a very limited set of relationships between images and sentences in their ground-truth annotations. This limited ground truth information forces us to use evaluation metrics based on binary relevance: given a sentence query we consider only one image as relevant. However, many other relevant images or captions may be present in the dataset. In this work, we propose two metrics that evaluate the degree of semantic relevance of retrieved items, independently of their annotated binary relevance. Additionally, we incorporate a novel strategy that uses an image captioning metric, CIDEr, to define a Semantic Adaptive Margin (SAM) to be optimized in a standard triplet loss. By incorporating our formulation to existing models, a large improvement is obtained in scenarios where available training data is limited. We also demonstrate that the performance on the annotated image-caption pairs is maintained while improving on other non-annotated relevant items when employing the full training set. The code for our new metric can be found at github. com/furkanbiten/ncsmetric and the model implementation at github. com/andrespmd/semanticadaptive_margin. |
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Virtual; Waikoloa; Hawai; USA; January 2022 |
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WACV |
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DAG; 600.155; 302.105; |
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Admin @ si @ BMG2022 |
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3663 |
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