Deep Neural Networks for Document Processing of Music Score Images

[EN] There is an increasing interest in the automatic digitization of medieval music documents. Despite efforts in this field, the detection of the different layers of information on these documents still poses difficulties. The use of Deep Neural Networks techniques has reported outstanding results in many areas related to computer vision. Consequently, in this paper, we study the so-called Convolutional Neural Networks (CNN) for performing the automatic document processing of music score images. This process is focused on layering the image into its constituent parts (namely, background, staff lines, music notes, and text) by training a classifier with examples of these parts. A comprehensive experimentation in terms of the configuration of the networks was carried out, which illustrates interesting results as regards to both the efficiency and effectiveness of these models. In addition, a cross-manuscript adaptation experiment was presented in which the networks are evaluated on a different manuscript from the one they were trained. The results suggest that the CNN is capable of adapting its knowledge, and so starting from a pre-trained CNN reduces (or eliminates) the need for new labeled data.This work was supported by the Social Sciences and Humanities Research Council of Canada, and Universidad de Alicante through grant GRE-16-04.Calvo-Zaragoza, J.; Castellanos, F.; Vigliensoni, G.; Fujinaga, I. (2018). Deep Neural Networks for Document Processing of Music Score Images. Applied Sciences. 8(5). https://doi.org/10.3390/app8050654S85Bainbridge, D., & Bell, T. (2001). Computers and the Humanities, 35(2), 95-121. doi:10.1023/a:1002485918032Byrd, D., & Simonsen, J. G. (2015). Towards a Standard Testbed for Optical Music Recognition: Definitions, Metrics, and Page Images. Journal of New Music Research, 44(3), 169-195. doi:10.1080/09298215.2015.1045424LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444. doi:10.1038/nature14539Rebelo, A., Fujinaga, I., Paszkiewicz, F., Marcal, A. R. S., Guedes, C., & Cardoso, J. S. (2012). Optical music recognition: state-of-the-art and open issues. International Journal of Multimedia Information Retrieval, 1(3), 173-190. doi:10.1007/s13735-012-0004-6Louloudis, G., Gatos, B., Pratikakis, I., & Halatsis, C. (2008). Text line detection in handwritten documents. Pattern Recognition, 41(12), 3758-3772. doi:10.1016/j.patcog.2008.05.011Montagner, I. S., Hirata, N. S. T., & Hirata, R. (2017). Staff removal using image operator learning. Pattern Recognition, 63, 310-320. doi:10.1016/j.patcog.2016.10.002Calvo-Zaragoza, J., Micó, L., & Oncina, J. (2016). Music staff removal with supervised pixel classification. International Journal on Document Analysis and Recognition (IJDAR), 19(3), 211-219. doi:10.1007/s10032-016-0266-2Calvo-Zaragoza, J., Pertusa, A., & Oncina, J. (2017). Staff-line detection and removal using a convolutional neural network. Machine Vision and Applications, 28(5-6), 665-674. doi:10.1007/s00138-017-0844-4Shelhamer, E., Long, J., & Darrell, T. (2017). Fully Convolutional Networks for Semantic Segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(4), 640-651. doi:10.1109/tpami.2016.2572683Kato, Z. (2011). Markov Random Fields in Image Segmentation. Foundations and Trends® in Signal Processing, 5(1-2), 1-155. doi:10.1561/2000000035Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11), 2278-2324. doi:10.1109/5.72679

Modèle de dégradation d’images de documents anciens pour la génération de données semi-synthétiques

In the last two decades, the increase in document image digitization projects results in scientific effervescence for conceiving document image processing and analysis algorithms (handwritten recognition, structure document analysis, spotting and indexing / retrieval graphical elements, etc.). A number of successful algorithms are based on learning (supervised, semi-supervised or unsupervised). In order to train such algorithms and to compare their performances, the scientific community on document image analysis needs many publicly available annotated document image databases. Their contents must be exhaustive enough to be representative of the possible variations in the documents to process / analyze. To create real document image databases, one needs an automatic or a manual annotation process. The performance of an automatic annotation process is proportional to the quality and completeness of these databases, and therefore annotation remains largely manual. Regarding the manual process, it is complicated, subjective, and tedious. To overcome such difficulties, several crowd-sourcing initiatives have been proposed, and some of them being modelled as a game to be more attractive. Such processes reduce significantly the price andsubjectivity of annotation, but difficulties still exist. For example, transcription and textline alignment have to be carried out manually. Since the 1990s, alternative document image generation approaches have been proposed including in generating semi-synthetic document images mimicking real ones. Semi-synthetic document image generation allows creating rapidly and cheaply benchmarking databases for evaluating the performances and trainingdocument processing and analysis algorithms. In the context of the project DIGIDOC (Document Image diGitisation with Interactive DescriptiOn Capability) funded by ANR (Agence Nationale de la Recherche), we focus on semi-synthetic document image generation adapted to ancient documents. First, we investigate new degradation models or adapt existing degradation models to ancient documents such as bleed-through model, distortion model, character degradation model, etc. Second, we apply such degradation models to generate semi-synthetic document image databases for performance evaluation (e.g the competition ICDAR2013, GREC2013) or for performance improvement (by re-training a handwritten recognition system, a segmentation system, and a binarisation system). This research work raises many collaboration opportunities with other researchers to share our experimental results with our scientific community. This collaborative work also helps us to validate our degradation models and to prove the efficiency of semi-synthetic document images for performance evaluation and re-training.Le nombre important de campagnes de numérisation mises en place ces deux dernières décennies a entraîné une effervescence scientifique ayant mené à la création de nombreuses méthodes pour traiter et/ou analyser ces images de documents (reconnaissance d’écriture, analyse de la structure de documents, détection/indexation et recherche d’éléments graphiques, etc.). Un bon nombre de ces approches est basé sur un apprentissage (supervisé, semi supervisé ou non supervisé). Afin de pouvoir entraîner les algorithmes correspondants et en comparer les performances, la communauté scientifique a un fort besoin de bases publiques d’images de documents avec la vérité-terrain correspondante, et suffisamment exhaustive pour contenir des exemples représentatifs du contenu des documents à traiter ou analyser. La constitution de bases d’images de documents réels nécessite d’annoter les données (constituer la vérité terrain). Les performances des approches récentes d’annotation automatique étant très liées à la qualité et à l’exhaustivité des données d’apprentissage, ce processus d’annotation reste très largement manuel. Ce processus peut s’avérer complexe, subjectif et fastidieux. Afin de tenter de pallier à ces difficultés, plusieurs initiatives de crowdsourcing ont vu le jour ces dernières années, certaines sous la forme de jeux pour les rendre plus attractives. Si ce type d’initiatives permet effectivement de réduire le coût et la subjectivité des annotations, reste un certain nombre de difficultés techniques difficiles à résoudre de manière complètement automatique, par exemple l’alignement de la transcription et des lignes de texte automatiquement extraites des images. Une alternative à la création systématique de bases d’images de documents étiquetées manuellement a été imaginée dès le début des années 90. Cette alternative consiste à générer des images semi-synthétiques imitant les images réelles. La génération d’images de documents semi-synthétiques permet de constituer rapidement un volume de données important et varié, répondant ainsi aux besoins de la communauté pour l’apprentissage et l’évaluation de performances de leurs algorithmes. Dans la cadre du projet DIGIDOC (Document Image diGitisation with Interactive DescriptiOn Capability) financé par l’ANR (Agence Nationale de la Recherche), nous avons mené des travaux de recherche relatifs à la génération d’images de documents anciens semi-synthétiques. Le premier apport majeur de nos travaux réside dans la création de plusieurs modèles de dégradation permettant de reproduire de manière synthétique des déformations couramment rencontrées dans les images de documents anciens (dégradation de l’encre, déformation du papier, apparition de la transparence, etc.). Le second apport majeur de ces travaux de recherche est la mise en place de plusieurs bases d’images semi-synthétiques utilisées dans des campagnes de test (compétition ICDAR2013, GREC2013) ou pour améliorer par ré-apprentissage les résultats de méthodes de reconnaissance de caractères, de segmentation ou de binarisation. Ces travaux ont abouti sur plusieurs collaborations nationales et internationales, qui se sont soldées en particulier par plusieurs publications communes. Notre but est de valider de manière la plus objective possible, et en collaboration avec la communauté scientifique concernée, l’intérêt des images de documents anciens semi-synthétiques générées pour l’évaluation de performances et le ré-apprentissage

Proceedings of the 4th International Workshop on Reading Music Systems

The International Workshop on Reading Music Systems (WoRMS) is a workshop that tries to connect researchers who develop systems for reading music, such as in the field of Optical Music Recognition, with other researchers and practitioners that could benefit from such systems, like librarians or musicologists. The relevant topics of interest for the workshop include, but are not limited to: Music reading systems; Optical music recognition; Datasets and performance evaluation; Image processing on music scores; Writer identification; Authoring, editing, storing and presentation systems for music scores; Multi-modal systems; Novel input-methods for music to produce written music; Web-based Music Information Retrieval services; Applications and projects; Use-cases related to written music. These are the proceedings of the 4th International Workshop on Reading Music Systems, held online on Nov. 18th 2022.Comment: Proceedings edited by Jorge Calvo-Zaragoza, Alexander Pacha and Elona Shatr

Pattern Recognition

A wealth of advanced pattern recognition algorithms are emerging from the interdiscipline between technologies of effective visual features and the human-brain cognition process. Effective visual features are made possible through the rapid developments in appropriate sensor equipments, novel filter designs, and viable information processing architectures. While the understanding of human-brain cognition process broadens the way in which the computer can perform pattern recognition tasks. The present book is intended to collect representative researches around the globe focusing on low-level vision, filter design, features and image descriptors, data mining and analysis, and biologically inspired algorithms. The 27 chapters coved in this book disclose recent advances and new ideas in promoting the techniques, technology and applications of pattern recognition

Semantics-enriched workflow creation and management system with an application to document image analysis and recognition

Scientific workflow systems are an established means to model and execute experiments or processing pipelines. Nevertheless, designing workflows can be a daunting task for users due to the complexities of the systems and the sheer number of available processing nodes, each having different compatibility/applicability characteristics. This Thesis explores how concepts of the Semantic Web can be used to augment workflow systems in order to assist researchers as well as non-expert users in creating valid and effective workflows. A prototype workflow creation/management system has been developed, including components for ontology modelling, workflow composition, and workflow repositories. Semantics are incorporated as a lightweight layer, permeating all aspects of the system and workflows, including retrieval, composition, and validation. Document image analysis and recognition is used as a representative application domain to evaluate the validity of the system. A new semantic model is proposed, covering a wide range of aspects of the target domain and adjacent fields. Real-world use cases demonstrate the assistive features and the automated workflow creation. On that basis, the prototype workflow creation/management system is compared to other state-of-the-art workflow systems and it is shown how those could benefit from the semantic model. The Thesis concludes with a discussion on how a complete infrastructure based on semantics-enriched datasets, workflow systems, and sharing platforms could represent the next step in automation within document image analysis and other domains

Évaluation de la qualité des documents anciens numérisés

Les travaux de recherche présentés dans ce manuscrit décrivent plusieurs apports au thème de l évaluation de la qualité d images de documents numérisés. Pour cela nous proposons de nouveaux descripteurs permettant de quantifier les dégradations les plus couramment rencontrées sur les images de documents numérisés. Nous proposons également une méthodologie s appuyant sur le calcul de ces descripteurs et permettant de prédire les performances d algorithmes de traitement et d analyse d images de documents. Les descripteurs sont définis en analysant l influence des dégradations sur les performances de différents algorithmes, puis utilisés pour créer des modèles de prédiction à l aide de régresseurs statistiques. La pertinence, des descripteurs proposés et de la méthodologie de prédiction, est validée de plusieurs façons. Premièrement, par la prédiction des performances de onze algorithmes de binarisation. Deuxièmement par la création d un processus automatique de sélection de l algorithme de binarisation le plus performant pour chaque image. Puis pour finir, par la prédiction des performances de deux OCRs en fonction de l importance du défaut de transparence (diffusion de l encre du recto sur le verso d un document). Ce travail sur la prédiction des performances d algorithmes est aussi l occasion d aborder les problèmes scientifiques liés à la création de vérités-terrains et d évaluation de performances.This PhD. thesis deals with quality evaluation of digitized document images. In order to measure the quality of a document image, we propose to create new features dedicated to the characterization of most commons degradations. We also propose to use these features to create prediction models able to predict the performances of different types of document analysis algorithms. The features are defined by analyzing the impact of a specific degradation on the results of an algorithm and then used to create statistical regressors.The relevance of the proposed features and predictions models, is analyzed in several experimentations. The first one aims to predict the performance of different binarization methods. The second experiment aims to create an automatic procedure able to select the best binarization method for each image. At last, the third experiment aims to create a prediction model for two commonly used OCRs. This work on performance prediction algorithms is also an opportunity to discuss the scientific problems of creating ground-truth for performance evaluation.BORDEAUX1-Bib.electronique (335229901) / SudocSudocFranceF