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Author |
Liu Wenyin; Josep Llados; Jean-Marc Ogier |
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Title  |
Graphics Recognition. Recent Advances and New Opportunities. |
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2008 |
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7th International Workshop, Selected Papers, |
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5046 |
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Curitiba (Brazil) |
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978-3-540-88184-1 |
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GREC |
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DAG |
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DAG @ dag @ WLO2008 |
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1012 |
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Author |
Josep Llados; Young-Bin Kwon |
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Title |
Graphics Recognition. Recent Advances and Perspectives |
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Miscellaneous |
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2004 |
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LNCS 3080, ISBN: 3–540–22478–5 |
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Springer-Verlag |
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DAG @ dag @ LlK2004 |
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515 |
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Author |
W. Liu; Josep Llados |
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Title |
Graphics Recognition. Ten Years Review and Future Perspectives |
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2006 |
Publication |
6th International Workshop |
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3926 |
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Hong Kong (China) |
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GREC |
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DAG |
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DAG @ dag @ LiL2006 |
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800 |
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Author |
Jean-Marc Ogier; Wenyin Liu; Josep Llados (eds) |
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Title |
Graphics Recognition: Achievements, Challenges, and Evolution |
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2010 |
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8th International Workshop GREC 2009. |
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6020 |
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La Rochelle |
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Springer Link |
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Jean-Marc Ogier; Wenyin Liu; Josep Llados |
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Lecture Notes in Computer Science |
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978-3-642-13727-3 |
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GREC |
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DAG |
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Admin @ si @ OLL2010 |
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1976 |
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Author |
Ayan Banerjee; Sanket Biswas; Josep Llados; Umapada Pal |
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Title |
GraphKD: Exploring Knowledge Distillation Towards Document Object Detection with Structured Graph Creation |
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Miscellaneous |
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2024 |
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Arxiv |
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Object detection in documents is a key step to automate the structural elements identification process in a digital or scanned document through understanding the hierarchical structure and relationships between different elements. Large and complex models, while achieving high accuracy, can be computationally expensive and memory-intensive, making them impractical for deployment on resource constrained devices. Knowledge distillation allows us to create small and more efficient models that retain much of the performance of their larger counterparts. Here we present a graph-based knowledge distillation framework to correctly identify and localize the document objects in a document image. Here, we design a structured graph with nodes containing proposal-level features and edges representing the relationship between the different proposal regions. Also, to reduce text bias an adaptive node sampling strategy is designed to prune the weight distribution and put more weightage on non-text nodes. We encode the complete graph as a knowledge representation and transfer it from the teacher to the student through the proposed distillation loss by effectively capturing both local and global information concurrently. Extensive experimentation on competitive benchmarks demonstrates that the proposed framework outperforms the current state-of-the-art approaches. The code will be available at: this https URL. |
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DAG |
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Admin @ si @ BBL2024b |
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4023 |
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Author |
Ricard Coll; Alicia Fornes; Josep Llados |
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Title |
Graphological Analysis of Handwritten Text Documents for Human Resources Recruitment |
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Conference Article |
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Year |
2009 |
Publication |
10th International Conference on Document Analysis and Recognition |
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1081–1085 |
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The use of graphology in recruitment processes has become a popular tool in many human resources companies. This paper presents a model that links features from handwritten images to a number of personality characteristics used to measure applicant aptitudes for the job in a particular hiring scenario. In particular we propose a model of measuring active personality and leadership of the writer. Graphological features that define such a profile are measured in terms of document and script attributes like layout configuration, letter size, shape, slant and skew angle of lines, etc. After the extraction, data is classified using a neural network. An experimental framework with real samples has been constructed to illustrate the performance of the approach. |
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Barcelona, Spain |
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1520-5363 |
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978-1-4244-4500-4 |
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ICDAR |
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DAG |
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DAG @ dag @ CFL2009 |
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1221 |
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Author |
Josep Llados; Jaime Lopez-Krahe; Enric Marti |
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Title |
Hand drawn document understanding using the straight line Hough transform and graph matching |
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Conference Article |
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1996 |
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Proceedings of the 13th International Pattern Recognition Conference (ICPR’96) |
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2 |
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497-501 |
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This paper presents a system to understand hand drawn architectural drawings in a CAD environment. The procedure is to identify in a floor plan the building elements, stored in a library of patterns, and their spatial relationships. The vectorized input document and the patterns to recognize are represented by attributed graphs. To recognize the patterns as such, we apply a structural approach based on subgraph isomorphism techniques. In spite of their value, graph matching techniques do not recognize adequately those building elements characterized by hatching patterns, i.e. walls. Here we focus on the recognition of hatching patterns and develop a straight line Hough transform based method in order to detect the regions filled in with parallel straight fines. This allows not only to recognize filling patterns, but it actually reduces the computational load associated with the subgraph isomorphism computation. The result is that the document can be redrawn by editing all the patterns recognized |
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Vienna , Austria |
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DAG;IAM; |
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IAM @ iam @ LLM1996 |
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1579 |
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Author |
Alicia Fornes; Sergio Escalera; Josep Llados; Gemma Sanchez; Joan Mas |
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Title |
Hand Drawn Symbol Recognition by Blurred Shape Model Descriptor and a Multiclass Classifier |
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Book Chapter |
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Year |
2008 |
Publication |
Graphics Recognition: Recent Advances and New Opportunities |
Abbreviated Journal |
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5046 |
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30–40 |
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W. Liu, J. Llados, J.M. Ogier |
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DAG; HUPBA; MILAB |
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BCNPCL @ bcnpcl @ FEL2008 |
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989 |
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Author |
Ernest Valveny; Enric Marti |
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Hand-drawn symbol recognition in graphic documents using deformable template matching and a Bayesian framework |
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2000 |
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Proc. 15th Int Pattern Recognition Conf |
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2 |
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239-242 |
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Hand-drawn symbols can take many different and distorted shapes from their ideal representation. Then, very flexible methods are needed to be able to handle unconstrained drawings. We propose here to extend our previous work in hand-drawn symbol recognition based on a Bayesian framework and deformable template matching. This approach gets flexibility enough to fit distorted shapes in the drawing while keeping fidelity to the ideal shape of the symbol. In this work, we define the similarity measure between an image and a symbol based on the distance from every pixel in the image to the lines in the symbol. Matching is carried out using an implementation of the EM algorithm. Thus, we can improve recognition rates and computation time with respect to our previous formulation based on a simulated annealing algorithm. |
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0-7695-0750-6 |
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DAG;IAM; |
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IAM @ iam @ VAM2000 |
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1656 |
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Juan Ignacio Toledo; Sounak Dey; Alicia Fornes; Josep Llados |
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Title |
Handwriting Recognition by Attribute embedding and Recurrent Neural Networks |
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2017 |
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14th International Conference on Document Analysis and Recognition |
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1038-1043 |
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Handwriting recognition consists in obtaining the transcription of a text image. Recent word spotting methods based on attribute embedding have shown good performance when recognizing words. However, they are holistic methods in the sense that they recognize the word as a whole (i.e. they find the closest word in the lexicon to the word image). Consequently,
these kinds of approaches are not able to deal with out of vocabulary words, which are common in historical manuscripts. Also, they cannot be extended to recognize text lines. In order to address these issues, in this paper we propose a handwriting recognition method that adapts the attribute embedding to sequence learning. Concretely, the method learns the attribute embedding of patches of word images with a convolutional neural network. Then, these embeddings are presented as a sequence to a recurrent neural network that produces the transcription. We obtain promising results even without the use of any kind of dictionary or language model |
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DAG; 600.097; 601.225; 600.121 |
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Admin @ si @ TDF2017 |
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3055 |
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