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Author  |
Adria Molina; Pau Riba; Lluis Gomez; Oriol Ramos Terrades; Josep Llados |
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Title |
Date Estimation in the Wild of Scanned Historical Photos: An Image Retrieval Approach |
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Conference Article |
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2021 |
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16th International Conference on Document Analysis and Recognition |
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12822 |
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306-320 |
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This paper presents a novel method for date estimation of historical photographs from archival sources. The main contribution is to formulate the date estimation as a retrieval task, where given a query, the retrieved images are ranked in terms of the estimated date similarity. The closer are their embedded representations the closer are their dates. Contrary to the traditional models that design a neural network that learns a classifier or a regressor, we propose a learning objective based on the nDCG ranking metric. We have experimentally evaluated the performance of the method in two different tasks: date estimation and date-sensitive image retrieval, using the DEW public database, overcoming the baseline methods. |
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Lausanne; Suissa; September 2021 |
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DAG; 600.121; 600.140; 110.312 |
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Admin @ si @ MRG2021b |
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3571 |
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Author |
Adria Molina; Lluis Gomez; Oriol Ramos Terrades; Josep Llados |
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Title |
A Generic Image Retrieval Method for Date Estimation of Historical Document Collections |
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Conference Article |
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2022 |
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Document Analysis Systems.15th IAPR International Workshop, (DAS2022) |
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13237 |
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583–597 |
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Date estimation; Document retrieval; Image retrieval; Ranking loss; Smooth-nDCG |
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Date estimation of historical document images is a challenging problem, with several contributions in the literature that lack of the ability to generalize from one dataset to others. This paper presents a robust date estimation system based in a retrieval approach that generalizes well in front of heterogeneous collections. We use a ranking loss function named smooth-nDCG to train a Convolutional Neural Network that learns an ordination of documents for each problem. One of the main usages of the presented approach is as a tool for historical contextual retrieval. It means that scholars could perform comparative analysis of historical images from big datasets in terms of the period where they were produced. We provide experimental evaluation on different types of documents from real datasets of manuscript and newspaper images. |
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La Rochelle, France; May 22–25, 2022 |
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DAS |
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DAG; 600.140; 600.121 |
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Admin @ si @ MGR2022 |
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3694 |
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Author |
Adarsh Tiwari; Sanket Biswas; Josep Llados |
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Title |
Can Pre-trained Language Models Help in Understanding Handwritten Symbols? |
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Conference Article |
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2023 |
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17th International Conference on Document Analysis and Recognition |
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14193 |
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199–211 |
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The emergence of transformer models like BERT, GPT-2, GPT-3, RoBERTa, T5 for natural language understanding tasks has opened the floodgates towards solving a wide array of machine learning tasks in other modalities like images, audio, music, sketches and so on. These language models are domain-agnostic and as a result could be applied to 1-D sequences of any kind. However, the key challenge lies in bridging the modality gap so that they could generate strong features beneficial for out-of-domain tasks. This work focuses on leveraging the power of such pre-trained language models and discusses the challenges in predicting challenging handwritten symbols and alphabets. |
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San Jose; CA; USA; August 2023 |
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DAG |
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Admin @ si @ TBL2023 |
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3908 |
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Author |
A.Nicolaou; Andrew Bagdanov; Marcus Liwicki; Dimosthenis Karatzas |
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Title |
Sparse Radial Sampling LBP for Writer Identification |
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Conference Article |
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2015 |
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13th International Conference on Document Analysis and Recognition ICDAR2015 |
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716-720 |
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In this paper we present the use of Sparse Radial Sampling Local Binary Patterns, a variant of Local Binary Patterns (LBP) for text-as-texture classification. By adapting and extending the standard LBP operator to the particularities of text we get a generic text-as-texture classification scheme and apply it to writer identification. In experiments on CVL and ICDAR 2013 datasets, the proposed feature-set demonstrates State-Of-the-Art (SOA) performance. Among the SOA, the proposed method is the only one that is based on dense extraction of a single local feature descriptor. This makes it fast and applicable at the earliest stages in a DIA pipeline without the need for segmentation, binarization, or extraction of multiple features. |
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Nancy; France; August 2015 |
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DAG; 600.077 |
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Admin @ si @ NBL2015 |
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2692 |
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Author |
A.Kesidis; Dimosthenis Karatzas |
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Title |
Logo and Trademark Recognition |
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2014 |
Publication |
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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Author |
A. Pujol; Jordi Vitria; Petia Radeva; X. Binefa; Robert Benavente; Ernest Valveny; Craig Von Land |
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Title |
Real time pharmaceutical product recognition using color and shape indexing. |
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1999 |
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Proceedings of the 2nd International Workshop on European Scientific and Industrial Collaboration (WESIC´99), Promotoring Advanced Technologies in Manufacturing. |
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Wales |
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OR;MILAB;DAG;CIC;MV |
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BCNPCL @ bcnpcl @ PVR1999 |
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24 |
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