Výsledky vyhledávání - "Jérôme Louradour"

Multilevel ensembling for local climate zones classification

Autor: Jérôme Louradour, Ivan Tankoyeu, Roel Heremans, Christian Debes, S. Sukhanov, D. Trofimova

Publikováno v: IGARSS

This paper presents an end-to-end system for automatic local climate zones classification of various types of urban environment. For that we perform fusion of multispectral images from Landsat-8 and Sentinel-2 satellites with site description extract

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::2542742141761484e8f5e480f1c9ac85
https://doi.org/10.1109/igarss.2017.8127173

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Learning text-line localization with shared and local regression neural networks

Autor: Christian Wolf, Christopher Kermorvant, Bastien Moysset, Jérôme Louradour

Publikováno v: International Conference on Frontiers in Handwriting Recognition
International Conference on Frontiers in Handwriting Recognition, Oct 2016, Shenzhen, China
ICFHR

International audience; Text line detection and localisation is a crucial step for full page document analysis, but still suffers from heterogeneity of real life documents. In this paper, we present a novel approach for text line localisation based o

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3bb92fcc44e147df1d417e1bc51aa0ab
https://hal.archives-ouvertes.fr/hal-01345713

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Scan, Attend and Read: End-to-End Handwritten Paragraph Recognition with MDLSTM Attention

Autor: Jérôme Louradour, Theodore Bluche, Ronaldo Messina

Publikováno v: ICDAR

We present an attention-based model for end-to-end handwriting recognition. Our system does not require any segmentation of the input paragraph. The model is inspired by the differentiable attention models presented recently for speech recognition, i

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8510cef5c8364a4872efb64ca8c9ffbe
http://arxiv.org/abs/1604.03286

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Feature Space Mahalanobis Sequence Kernels: Application to SVM Speaker Verification

Autor: Jérôme Louradour, K. Daoudi, Francis Bach

Publikováno v: IEEE Transactions on Audio, Speech and Language Processing
IEEE Transactions on Audio, Speech and Language Processing, 2007, 15 (8), pp.2465--2475

The generalized linear discriminant sequence (GLDS) kernel has been shown to provide very good performance and efficiency at the NIST Speaker Recognition Evaluations (SRE) in the last few years. This kernel is based on an explicit map of polynomial e

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::36271bbc8a0173890a0f009e212659cf
https://doi.org/10.1109/tasl.2007.905147

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Paragraph text segmentation into lines with Recurrent Neural Networks

Autor: Jérôme Louradour, Christopher Kermorvant, Bastien Moysset, Christian Wolf

Publikováno v: International Conference on Document Analysis and Recognition (ICDAR)
International Conference on Document Analysis and Recognition
International Conference on Document Analysis and Recognition, Aug 2015, Tunisia, Tunisia
ICDAR

International audience; The detection of text lines, as a first processing step, is critical in all Text Recognition systems. State-of-the-art methods to locate lines of text are based on handcrafted heuristics fine-tuned by the Image Processing Comm

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c75f213519032e97ffff8be8589f44da
https://hal.archives-ouvertes.fr/hal-01151760

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Space Displacement Localization Neural Networks to locate origin points of handwritten text lines in historical documents

Autor: Christian Wolf, Pierre Adam, Jérôme Louradour, Bastien Moysset

Publikováno v: HIP@ICDAR
ICDAR 2015 Workshop on Historical Document Imaging and Processing
ICDAR 2015 Workshop on Historical Document Imaging and Processing, Aug 2015, Nancy, France

International audience; We describe a new method for detecting and localizing multiple objects in an image using context aware deep neural networks. Common architectures either proceed locally per pixel-wise sliding-windows, or globally by predicting

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ec6184ae79cce58b8d7fa3455239df32
https://doi.org/10.1145/2809544.2809546

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Segmentation-free handwritten Chinese text recognition with LSTM-RNN

Autor: Ronaldo Messina, Jérôme Louradour

Publikováno v: ICDAR

We present initial results on the use of Multi-Dimensional Long-Short Term Memory Recurrent Neural Networks (MDLSTM-RNN) in recognizing lines of handwritten Chinese text without explicit segmentation of the characters. In fact, most of Chinese text r

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::4d6b4be8eec2206c3da43181a0ae2c99
https://doi.org/10.1109/icdar.2015.7333746

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Framewise and CTC training of Neural Networks for handwriting recognition

Autor: Theodore Bluche, Jérôme Louradour, Christopher Kermorvant, Hermann Ney

Publikováno v: ICDAR

In recent years, Long Short-Term Memory Recurrent Neural Networks (LSTM-RNNs) trained with the Connectionist Temporal Classification (CTC) objective won many international handwriting recognition evaluations. The CTC algorithm is based on a forward-b

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::61dcf6a268ff1cd874be78e5c544cc41
https://doi.org/10.1109/icdar.2015.7333730

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Where to apply dropout in recurrent neural networks for handwriting recognition?

Autor: Jérôme Louradour, Theodore Bluche, Christopher Kermorvant

Publikováno v: ICDAR

The dropout technique is a data-driven regularization method for neural networks. It consists in randomly setting some activations from a given hidden layer to zero during training. Repeating the procedure for each training example, it is equivalent

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::1adef083d0309abed1b619d8b03f73aa
https://doi.org/10.1109/icdar.2015.7333848

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