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Author |
Giuseppe De Gregorio; Sanket Biswas; Mohamed Ali Souibgui; Asma Bensalah; Josep Llados; Alicia Fornes; Angelo Marcelli |
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Title |
A Few Shot Multi-representation Approach for N-Gram Spotting in Historical Manuscripts |
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Conference Article |
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Year |
2022 |
Publication |
Frontiers in Handwriting Recognition. International Conference on Frontiers in Handwriting Recognition (ICFHR2022) |
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13639 |
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3-12 |
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N-gram spotting; Few-shot learning; Multimodal understanding; Historical handwritten collections |
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Abstract |
Despite recent advances in automatic text recognition, the performance remains moderate when it comes to historical manuscripts. This is mainly because of the scarcity of available labelled data to train the data-hungry Handwritten Text Recognition (HTR) models. The Keyword Spotting System (KWS) provides a valid alternative to HTR due to the reduction in error rate, but it is usually limited to a closed reference vocabulary. In this paper, we propose a few-shot learning paradigm for spotting sequences of a few characters (N-gram) that requires a small amount of labelled training data. We exhibit that recognition of important n-grams could reduce the system’s dependency on vocabulary. In this case, an out-of-vocabulary (OOV) word in an input handwritten line image could be a sequence of n-grams that belong to the lexicon. An extensive experimental evaluation of our proposed multi-representation approach was carried out on a subset of Bentham’s historical manuscript collections to obtain some really promising results in this direction. |
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December 04 – 07, 2022; Hyderabad, India |
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ICFHR |
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DAG; 600.121; 600.162; 602.230; 600.140 |
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Admin @ si @ GBS2022 |
Serial |
3733 |
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Author |
Francesc Net; Marc Folia; Pep Casals; Lluis Gomez |
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Title |
Transductive Learning for Near-Duplicate Image Detection in Scanned Photo Collections |
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Conference Article |
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Year |
2023 |
Publication |
17th International Conference on Document Analysis and Recognition |
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14191 |
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3-17 |
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Image deduplication; Near-duplicate images detection; Transductive Learning; Photographic Archives; Deep Learning |
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Abstract |
This paper presents a comparative study of near-duplicate image detection techniques in a real-world use case scenario, where a document management company is commissioned to manually annotate a collection of scanned photographs. Detecting duplicate and near-duplicate photographs can reduce the time spent on manual annotation by archivists. This real use case differs from laboratory settings as the deployment dataset is available in advance, allowing the use of transductive learning. We propose a transductive learning approach that leverages state-of-the-art deep learning architectures such as convolutional neural networks (CNNs) and Vision Transformers (ViTs). Our approach involves pre-training a deep neural network on a large dataset and then fine-tuning the network on the unlabeled target collection with self-supervised learning. The results show that the proposed approach outperforms the baseline methods in the task of near-duplicate image detection in the UKBench and an in-house private dataset. |
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San Jose; CA; USA; August 2023 |
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ICDAR |
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Admin @ si @ NFC2023 |
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3859 |
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Author |
Josep Llados; Dorothea Blostein |
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Special Issue on Graphics Recognition |
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2007 |
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International Journal on Document Analysis and Recognition |
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IJDAR |
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9 |
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1 |
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1–2 |
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DAG @ dag @ LlB2007 |
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781 |
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Author |
Herve Locteau; Sebastien Mace; Ernest Valveny; Salvatore Tabbone |
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Title |
Extraction des pieces de un plan de habitation |
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Conference Article |
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2010 |
Publication |
Colloque Internacional Francophone de l´Ecrit et le Document |
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1–12 |
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In this article, a method to extract the rooms of an architectural floor plan image is described. We first present a line detection algorithm to extract long lines in the image. Those lines are analyzed to identify the existing walls. From this point, room extraction can be seen as a classical segmentation task for which each region corresponds to a room. The chosen resolution strategy consists in recursively decomposing the image until getting nearly convex regions. The notion of convexity is difficult to quantify, and the selection of separation lines can also be rough. Thus, we take advantage of knowledge associated to architectural floor plans in order to obtain mainly rectangular rooms. Preliminary tests on a set of real documents show promising results. |
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Sousse, Tunisia |
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CIFED |
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DAG |
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DAG @ dag @ LMV2010 |
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1440 |
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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 |
Jon Almazan; Ernest Valveny; Alicia Fornes |
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Title |
Deforming the Blurred Shape Model for Shape Description and Recognition |
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Conference Article |
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2011 |
Publication |
5th Iberian Conference on Pattern Recognition and Image Analysis |
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6669 |
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1-8 |
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This paper presents a new model for the description and recognition of distorted shapes, where the image is represented by a pixel density distribution based on the Blurred Shape Model combined with a non-linear image deformation model. This leads to an adaptive structure able to capture elastic deformations in shapes. This method has been evaluated using thee different datasets where deformations are present, showing the robustness and good performance of the new model. Moreover, we show that incorporating deformation and flexibility, the new model outperforms the BSM approach when classifying shapes with high variability of appearance. |
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Las Palmas de Gran Canaria. Spain |
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Springer-Verlag |
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Berlin |
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Jordi Vitria; Joao Miguel Raposo; Mario Hernandez |
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IbPRIA |
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DAG; |
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Admin @ si @ AVF2011 |
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1732 |
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Author |
S. Chanda; Umapada Pal; Oriol Ramos Terrades |
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Title |
Word-Wise Thai and Roman Script Identification |
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2009 |
Publication |
ACM Transactions on Asian Language Information Processing |
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TALIP |
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8 |
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3 |
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1-21 |
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In some Thai documents, a single text line of a printed document page may contain words of both Thai and Roman scripts. For the Optical Character Recognition (OCR) of such a document page it is better to identify, at first, Thai and Roman script portions and then to use individual OCR systems of the respective scripts on these identified portions. In this article, an SVM-based method is proposed for identification of word-wise printed Roman and Thai scripts from a single line of a document page. Here, at first, the document is segmented into lines and then lines are segmented into character groups (words). In the proposed scheme, we identify the script of a character group combining different character features obtained from structural shape, profile behavior, component overlapping information, topological properties, and water reservoir concept, etc. Based on the experiment on 10,000 data (words) we obtained 99.62% script identification accuracy from the proposed scheme. |
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1530-0226 |
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Admin @ si @ CPR2009f |
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1869 |
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Author |
H. Chouaib; Oriol Ramos Terrades; Salvatore Tabbone; F. Cloppet; N. Vincent |
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Title |
Feature Selection Combining Genetic Algorithm and Adaboost Classifiers |
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Conference Article |
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2008 |
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19th International Conference on Pattern Recognition |
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1-4 |
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Tampa, Florida |
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ICPR |
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Admin @ si @ CRT2008 |
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1872 |
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Author |
Salvatore Tabbone; Oriol Ramos Terrades; S. Barrat |
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Title |
Histogram of radon transform. A useful descriptor for shape retrieval |
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2008 |
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19th International Conference on Pattern Recognition |
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Tampa, Florida |
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Admin @ si @ TRB2008 |
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1876 |
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Author |
Muhammad Muzzamil Luqman; Jean-Yves Ramel; Josep Llados |
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Title |
Multilevel Analysis of Attributed Graphs for Explicit Graph Embedding in Vector Spaces |
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2013 |
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Graph Embedding for Pattern Analysis |
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1-26 |
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Ability to recognize patterns is among the most crucial capabilities of human beings for their survival, which enables them to employ their sophisticated neural and cognitive systems [1], for processing complex audio, visual, smell, touch, and taste signals. Man is the most complex and the best existing system of pattern recognition. Without any explicit thinking, we continuously compare, classify, and identify huge amount of signal data everyday [2], starting from the time we get up in the morning till the last second we fall asleep. This includes recognizing the face of a friend in a crowd, a spoken word embedded in noise, the proper key to lock the door, smell of coffee, the voice of a favorite singer, the recognition of alphabetic characters, and millions of more tasks that we perform on regular basis. |
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Springer New York |
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978-1-4614-4456-5 |
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Admin @ si @ LRL2013b |
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2271 |
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