SEDIQA: Sound Emitting Document Image Quality Assessment in a Reading Aid for the Visually Impaired

For visually impaired people (VIPs), the ability to convert text to sound can mean a new level of independence or the simple joy of a good book. With significant advances in optical character recognition (OCR) in recent years, a number of reading aids are appearing on the market. These reading aids convert images captured by a camera to text which can then be read aloud. However, all of these reading aids suffer from a key issue—the user must be able to visually target the text and capture an image of sufficient quality for the OCR algorithm to function—no small task for VIPs. In this work, a sound-emitting document image quality assessment metric (SEDIQA) is proposed which allows the user to hear the quality of the text image and automatically captures the best image for OCR accuracy. This work also includes testing of OCR performance against image degradations, to identify the most significant contributors to accuracy reduction. The proposed no-reference image quality assessor (NR-IQA) is validated alongside established NR-IQAs and this work includes insights into the performance of these NR-IQAs on document images. SEDIQA is found to consistently select the best image for OCR accuracy. The full system includes a document image enhancement technique which introduces improvements in OCR accuracy with an average increase of 22% and a maximum increase of 68%

Transcription of Spanish Historical Handwritten Documents with Deep Neural Networks

[EN] The digitization of historical handwritten document images is important for the preservation of cultural heritage. Moreover, the transcription of text images obtained from digitization is necessary to provide efficient information access to the content of these documents. Handwritten Text Recognition (HTR) has become an important research topic in the areas of image and computational language processing that allows us to obtain transcriptions from text images. State-of-the-art HTR systems are, however, far from perfect. One difficulty is that they have to cope with image noise and handwriting variability. Another difficulty is the presence of a large amount of Out-Of-Vocabulary (OOV) words in ancient historical texts. A solution to this problem is to use external lexical resources, but such resources might be scarce or unavailable given the nature and the age of such documents. This work proposes a solution to avoid this limitation. It consists of associating a powerful optical recognition system that will cope with image noise and variability, with a language model based on sub-lexical units that will model OOV words. Such a language modeling approach reduces the size of the lexicon while increasing the lexicon coverage. Experiments are first conducted on the publicly available Rodrigo dataset, which contains the digitization of an ancient Spanish manuscript, with a recognizer based on Hidden Markov Models (HMMs). They show that sub-lexical units outperform word units in terms of Word Error Rate (WER), Character Error Rate (CER) and OOV word accuracy rate. This approach is then applied to deep net classifiers, namely Bi-directional Long-Short Term Memory (BLSTMs) and Convolutional Recurrent Neural Nets (CRNNs). Results show that CRNNs outperform HMMs and BLSTMs, reaching the lowest WER and CER for this image dataset and significantly improving OOV recognition.Work partially supported by projects READ: Recognition and Enrichment of Archival Documents - 674943 (European Union's H2020) and CoMUN-HaT: Context, Multimodality and User Collaboration in Handwritten Text Processing - TIN2015-70924-C2-1-R (MINECO/FEDER), and a DGA-MRIS (Direction Generale de l'Armement - Mission pour la Recherche et l'Innovation Scientifique) scholarship.Granell, E.; Chammas, E.; Likforman-Sulem, L.; Martínez-Hinarejos, C.; Mokbel, C.; Cirstea, B. (2018). Transcription of Spanish Historical Handwritten Documents with Deep Neural Networks. Journal of imaging. 4(1). https://doi.org/10.3390/jimaging4010015S154

Segmentation et indexation d'objets complexes dans les images de bandes dessinées

In this thesis, we review, highlight and illustrate the challenges related to comic book image analysis in order to give to the reader a good overview about the last research progress in this field and the current issues. We propose three different approaches for comic book image analysis that are composed by several processing. The first approach is called "sequential'' because the image content is described in an intuitive way, from simple to complex elements using previously extracted elements to guide further processing. Simple elements such as panel text and balloon are extracted first, followed by the balloon tail and then the comic character position in the panel. The second approach addresses independent information extraction to recover the main drawback of the first approach : error propagation. This second method is called “independent” because it is composed by several specific extractors for each elements of the image without any dependence between them. Extra processing such as balloon type classification and text recognition are also covered. The third approach introduces a knowledge-driven and scalable system of comics image understanding. This system called “expert system” is composed by an inference engine and two models, one for comics domain and another one for image processing, stored in an ontology. This expert system combines the benefits of the two first approaches and enables high level semantic description such as the reading order of panels and text, the relations between the speech balloons and their speakers and the comic character identification.Dans ce manuscrit de thèse, nous détaillons et illustrons les différents défis scientifiques liés à l'analyse automatique d'images de bandes dessinées, de manière à donner au lecteur tous les éléments concernant les dernières avancées scientifiques en la matière ainsi que les verrous scientifiques actuels. Nous proposons trois approches pour l'analyse d'image de bandes dessinées. La première approche est dite "séquentielle'' car le contenu de l'image est décrit progressivement et de manière intuitive. Dans cette approche, les extractions se succèdent, en commençant par les plus simples comme les cases, le texte et les bulles qui servent ensuite à guider l'extraction d'éléments plus complexes tels que la queue des bulles et les personnages au sein des cases. La seconde approche propose des extractions indépendantes les unes des autres de manière à éviter la propagation d'erreur due aux traitements successifs. D'autres éléments tels que la classification du type de bulle et la reconnaissance de texte y sont aussi abordés. La troisième approche introduit un système fondé sur une base de connaissance a priori du contenu des images de bandes dessinées. Ce système permet de construire une description sémantique de l'image, dirigée par les modèles de connaissances. Il combine les avantages des deux approches précédentes et permet une description sémantique de haut niveau pouvant inclure des informations telles que l'ordre de lecture, la sémantique des bulles, les relations entre les bulles et leurs locuteurs ainsi que les interactions entre les personnages

Music Encoding Conference Proceedings 2021, 19–22 July, 2021 University of Alicante (Spain): Onsite & Online

Este documento incluye los artículos y pósters presentados en el Music Encoding Conference 2021 realizado en Alicante entre el 19 y el 22 de julio de 2022.Funded by project Multiscore, MCIN/AEI/10.13039/50110001103

Integrating passive ubiquitous surfaces into human-computer interaction

Mobile technologies enable people to interact with computers ubiquitously. This dissertation investigates how ordinary, ubiquitous surfaces can be integrated into human-computer interaction to extend the interaction space beyond the edge of the display. It turns out that acoustic and tactile features generated during an interaction can be combined to identify input events, the user, and the surface. In addition, it is shown that a heterogeneous distribution of different surfaces is particularly suitable for realizing versatile interaction modalities. However, privacy concerns must be considered when selecting sensors, and context can be crucial in determining whether and what interaction to perform.Mobile Technologien ermöglichen den Menschen eine allgegenwärtige Interaktion mit Computern. Diese Dissertation untersucht, wie gewöhnliche, allgegenwärtige Oberflächen in die Mensch-Computer-Interaktion integriert werden können, um den Interaktionsraum über den Rand des Displays hinaus zu erweitern. Es stellt sich heraus, dass akustische und taktile Merkmale, die während einer Interaktion erzeugt werden, kombiniert werden können, um Eingabeereignisse, den Benutzer und die Oberfläche zu identifizieren. Darüber hinaus wird gezeigt, dass eine heterogene Verteilung verschiedener Oberflächen besonders geeignet ist, um vielfältige Interaktionsmodalitäten zu realisieren. Bei der Auswahl der Sensoren müssen jedoch Datenschutzaspekte berücksichtigt werden, und der Kontext kann entscheidend dafür sein, ob und welche Interaktion durchgeführt werden soll