A Unified Multilingual Handwriting Recognition System using multigrams sub-lexical units

We address the design of a unified multilingual system for handwriting recognition. Most of multi- lingual systems rests on specialized models that are trained on a single language and one of them is selected at test time. While some recognition systems are based on a unified optical model, dealing with a unified language model remains a major issue, as traditional language models are generally trained on corpora composed of large word lexicons per language. Here, we bring a solution by con- sidering language models based on sub-lexical units, called multigrams. Dealing with multigrams strongly reduces the lexicon size and thus decreases the language model complexity. This makes pos- sible the design of an end-to-end unified multilingual recognition system where both a single optical model and a single language model are trained on all the languages. We discuss the impact of the language unification on each model and show that our system reaches state-of-the-art methods perfor- mance with a strong reduction of the complexity.Comment: preprin

Iterative Refinement of HMM and HCRF for Sequence Classification

International audienceWe propose a strategy for semi-supervised learning of Hidden-state Conditional Random Fields (HCRF) for signal classification. It builds on simple procedures for semi-supervised learning of Hidden Markov Models (HMM) and on strategies for learning a HCRF from a trained HMM system. The algorithm learns a generative system based on Hidden Markov models and a discriminative one based on HCRFs where each model is refined by the other in an iterative framework

Hybrid HMM and HCRF model for sequence classification

International audienceWe propose a hybrid model combining a generative model and a discriminative model for signal labelling and classification tasks, aiming at taking the best from each world. The idea is to focus the learning of the discriminative model on most likely state sequences as output by the generative model. This allows taking advantage of the usual increased accuracy of generative models on small training datasets and of discriminative models on large training datasets. We instantiate this framework with Hidden Markov Models and Hidden Conditional Random Fields. We validate our model on financial time series and on handwriting data

Classification et détection de figures chartistes par apprentissage statistique

This thesis deals with financial stock market analysis and is especially focused on chart pattern recognition. A chart pattern is a particular shape which has a predictive power; it is defined by theoretical rules. Detecting such patterns is difficult. There is an important gap between theory and practice; real patterns do not perfectly respect the theoretical rules. Moreover, chart patterns definition seems subjective; it depends on the financial expert. Finally, there is no large labeled datasets of chart patterns.We study classification and detection of chart patterns using statistical markovian systems. We focus on generative (Hidden Markov Models) and discriminative (Conditional Random Fields, Hidden CRFs) approaches which are standard technologies for sequential data recognition.We propose various strategies to learn accurate systems with small training sets. The first one blends HMMs and HCRFs in such a way that the modeling ability of the generative models is used to limit the overfitting of the discriminative ones. The second strategy, is a semi-supervised approach which learns jointly a HMM and a HCRF systems; it has some similarity with the well-known co-training algorithm.To design an accurate detection system dedicated to a particular financial expert, we propose a two level system where candidate patterns are first extracted from the financial stock-market using HMMs, and then they are confirmed as chart patterns or rejected by a SVM which uses an enriched representation of patterns. While the HMM system is learn once for every expert, the SVM level is trained with an active learning strategy to take into account the expert s own detection criteria.Cette thèse porte sur l'analyse de cours financiers et plus particulièrement sur la reconnaissance de figures chartistes qui sont des motifs possédant un potentiel prédictif. Bien que leur définition obéisse à des règles théoriques précises, leur détection pose problème. L écart entre la théorie et la pratique est importante ; les figures réelles ne respectent pas parfaitement les règles théoriques. La définition des figures semble subjective et dépendre de l expert financier. Enfin il n existe pas de corpus de données étiquetées.Nous avons étudié la classification et la détection de ces figures à l aide de systèmes statistiques markoviens génératifs (HMMs) et discriminants (CRFs et Hidden CRFs) qui sont des technologies de référence pour le traitement de séquences.Nous avons proposé plusieurs stratégies pour apprendre de façon robuste ces systèmes avec peu de données étiquetées. La première est une hybridation des HMMs et des HCRFs reposant sur l idée d exploiter les capacités de modélisation des HMMs afin de limiter le sur-apprentissage des modèles discriminants (HCRFs). La seconde est une approche semi-supervisée qui emprunte au co-training l idée de l apprentissage conjoint de deux systèmes, l un génératif, l autre discriminant.Afin de concevoir des systèmes de détection performants et adaptés à chaque expert, nous avons conçu un système à deux niveaux dans lequel des motifs d'un cours sont pré-sélectionnés par des HMMs puis confirmés ou infirmés par une SVM opérant sur une description enrichie des motifs. Le modèle SVM est appris par une stratégie d apprentissage actif pour personnaliser le système à un expert particulier.PARIS-BIUSJ-Mathématiques rech (751052111) / SudocSudocFranceF

G. Cazals, Yann Soullard, Raphaëlle Krummeich, « Bibliothèque numérique de droit normand », Carnet de la MRSH, https://mrsh.hypotheses.org

Blog hypothèse

CTCModel: a Keras Model for Connectionist Temporal Classification

We report an extension of a Keras Model, called CTCModel, to perform the Connection-ist Temporal Classification (CTC) in a transparent way. Combined with Recurrent Neural Networks, the Connectionist Temporal Classification is the reference method for dealing with unsegmented input sequences, i.e. with data that are a couple of observation and label sequences where each label is related to a subset of observation frames. CTCModel makes use of the CTC implementation in the Tensorflow backend for training and predicting sequences of labels using Keras. It consists of three branches made of Keras models: one for training, computing the CTC loss function; one for predicting, providing sequences of labels; and one for evaluating that returns standard metrics for analyzing sequences of predictions

CTCModel: a Keras Model for Connectionist Temporal Classification

We report an extension of a Keras Model, called CTCModel, to perform the Connection-ist Temporal Classification (CTC) in a transparent way. Combined with Recurrent Neural Networks, the Connectionist Temporal Classification is the reference method for dealing with unsegmented input sequences, i.e. with data that are a couple of observation and label sequences where each label is related to a subset of observation frames. CTCModel makes use of the CTC implementation in the Tensorflow backend for training and predicting sequences of labels using Keras. It consists of three branches made of Keras models: one for training, computing the CTC loss function; one for predicting, providing sequences of labels; and one for evaluating that returns standard metrics for analyzing sequences of predictions

G. Cazals, Yann Soullard, Raphaëlle Krummeich, « Bibliothèque numérique de droit normand », Carnet de la MRSH, https://mrsh.hypotheses.org

Blog hypothèse

G. Cazals, Yann Soullard, Raphaëlle Krummeich, « Bibliothèque numérique de droit normand », Carnet de la MRSH, https://mrsh.hypotheses.org

Blog hypothèse

Full-page music symbols recognition: state-of-the-art deep models comparison for handwritten and printed music scores

The paper is under consideration at Pattern Recognition LettersThe localization and classification of musical symbols on scanned or digital music scores pose significant challenges in Optical Music Recognition, such as similar musical symbol categories and a large number of overlapping tiny musical symbols within high-resolution music scores. Recently, deep learning-based techniques show promising results in addressing these challenges by leveraging object detection models. However, unclear directions in training and evaluation approaches, such as inconsistency between usage of full-page or cropped images, handling image scores at full-page level in high-resolution, reporting results on only specific object categories, missing comprehensive analysis with recent state-of-the-art object detection methods, cause a lack of benchmarking and analyzing the impact of proposed methods in music object recognition. To address these issues, we perform intensive analysis with recent object detection models, exploring effective ways of handling high-resolution images on existing benchmarks. Our goal is to narrow the gap between object detection models designed for common objects and relatively small images compared to music scores, and the unique challenges of music score recognition in terms of object size and resolution. We achieve state-of-the-art results across mAP and Weighted mAP on two challenging datasets, namely DeepScoresV2 and the MUSCIMA++ datasets, by demonstrating the effectiveness of this approach in both printed and handwritten music scores