Extraction of Projection Profile, Run-Histogram and Entropy Features Straight from Run-Length Compressed Text-Documents

Document Image Analysis, like any Digital Image Analysis requires identification and extraction of proper features, which are generally extracted from uncompressed images, though in reality images are made available in compressed form for the reasons such as transmission and storage efficiency. However, this implies that the compressed image should be decompressed, which indents additional computing resources. This limitation induces the motivation to research in extracting features directly from the compressed image. In this research, we propose to extract essential features such as projection profile, run-histogram and entropy for text document analysis directly from run-length compressed text-documents. The experimentation illustrates that features are extracted directly from the compressed image without going through the stage of decompression, because of which the computing time is reduced. The feature values so extracted are exactly identical to those extracted from uncompressed images.Comment: Published by IEEE in Proceedings of ACPR-2013. arXiv admin note: text overlap with arXiv:1403.778

Sparse Radial Sampling LBP for Writer Identification

In this paper we present the use of Sparse Radial Sampling Local Binary Patterns, a variant of Local Binary Patterns (LBP) for text-as-texture classification. By adapting and extending the standard LBP operator to the particularities of text we get a generic text-as-texture classification scheme and apply it to writer identification. In experiments on CVL and ICDAR 2013 datasets, the proposed feature-set demonstrates State-Of-the-Art (SOA) performance. Among the SOA, the proposed method is the only one that is based on dense extraction of a single local feature descriptor. This makes it fast and applicable at the earliest stages in a DIA pipeline without the need for segmentation, binarization, or extraction of multiple features.Comment: Submitted to the 13th International Conference on Document Analysis and Recognition (ICDAR 2015

Study to determine potential flight applications and human factors design guidelines for voice recognition and synthesis systems

A study was conducted to determine potential commercial aircraft flight deck applications and implementation guidelines for voice recognition and synthesis. At first, a survey of voice recognition and synthesis technology was undertaken to develop a working knowledge base. Then, numerous potential aircraft and simulator flight deck voice applications were identified and each proposed application was rated on a number of criteria in order to achieve an overall payoff rating. The potential voice recognition applications fell into five general categories: programming, interrogation, data entry, switch and mode selection, and continuous/time-critical action control. The ratings of the first three categories showed the most promise of being beneficial to flight deck operations. Possible applications of voice synthesis systems were categorized as automatic or pilot selectable and many were rated as being potentially beneficial. In addition, voice system implementation guidelines and pertinent performance criteria are proposed. Finally, the findings of this study are compared with those made in a recent NASA study of a 1995 transport concept

Advances in Image Processing, Analysis and Recognition Technology

For many decades, researchers have been trying to make computers’ analysis of images as effective as the system of human vision is. For this purpose, many algorithms and systems have previously been created. The whole process covers various stages, including image processing, representation and recognition. The results of this work can be applied to many computer-assisted areas of everyday life. They improve particular activities and provide handy tools, which are sometimes only for entertainment, but quite often, they significantly increase our safety. In fact, the practical implementation of image processing algorithms is particularly wide. Moreover, the rapid growth of computational complexity and computer efficiency has allowed for the development of more sophisticated and effective algorithms and tools. Although significant progress has been made so far, many issues still remain, resulting in the need for the development of novel approaches

Apprentissage profond de formes manuscrites pour la reconnaissance et le repérage efficace de l'écriture dans les documents numérisés

Malgré les efforts importants de la communauté d’analyse de documents, définir une representation robuste pour les formes manuscrites demeure un défi de taille. Une telle representation ne peut pas être définie explicitement par un ensemble de règles, et doit plutôt être obtenue avec une extraction intelligente de caractéristiques de haut niveau à partir d’images de documents. Dans cette thèse, les modèles d’apprentissage profond sont investigués pour la representation automatique de formes manuscrites. Les représentations proposées par ces modèles sont utilisées pour définir un système de reconnaissance et de repérage de mots individuels dans les documents. Le choix de traiter les mots individuellement est motivé par le fait que n’importe quel texte peut être segmenté en un ensemble de mots séparés. Dans une première contribution, une représentation non supervisée profonde est proposée pour la tâche de repérage de mots manuscrits. Cette représentation se base sur l’algorithme de regroupement spherical k-means, qui est employé pour construire une hiérarchie de fonctions paramétriques encodant les images de documents. Les avantages de cette représentation sont multiples. Tout d’abord, elle est définie de manière non supervisée, ce qui évite la nécessité d’avoir des données annotées pour l’entraînement. Ensuite, elle se calcule rapidement et est de taille compacte, permettant ainsi de repérer des mots efficacement. Dans une deuxième contribution, un modèle de bout en bout est développé pour la reconnaissance de mots manuscrits. Ce modèle est composé d’un réseau de neurones convolutifs qui prend en entrée l’image d’un mot et produit en sortie une représentation du texte reconnu. Ce texte est représenté sous la forme d’un ensemble de sous-sequences bidirectionnelles de caractères formant une hiérarchie. Cette représentation se distingue des approches existantes dans la littérature et offre plusieurs avantages par rapport à celles-ci. Notamment, elle est binaire et a une taille fixe, ce qui la rend robuste à la taille du texte. Par ailleurs, elle capture la distribution des sous-séquences de caractères dans le corpus d’entraînement, et permet donc au modèle entraîné de transférer cette connaissance à de nouveaux mots contenant les memes sous-séquences. Dans une troisième et dernière contribution, un modèle de bout en bout est proposé pour résoudre simultanément les tâches de repérage et de reconnaissance. Ce modèle intègre conjointement les textes et les images de mots dans un seul espace vectoriel. Une image est projetée dans cet espace via un réseau de neurones convolutifs entraîné à détecter les différentes forms de caractères. De même, un mot est projeté dans cet espace via un réseau de neurones récurrents. Le modèle proposé est entraîné de manière à ce que l’image d’un mot et son texte soient projetés au même point. Dans l’espace vectoriel appris, les tâches de repérage et de reconnaissance peuvent être traitées efficacement comme un problème de recherche des plus proches voisins

ISTRAŽIVANJE O POVEZIVANJU ENTITETA ZA SPECIFIČNE DOMENE S HETEROGENIM INFORMACIJSKIM MREŽAMA

Entity linking is a task of extracting information that links the mentioned entity in a collection of text with their similar knowledge base as well as it is the task of allocating unique identity to various entities such as locations, individuals and companies. Knowledgebase (KB) is used to optimize the information collection, organization and for retrieval of information. Heterogeneous information networks (HIN) comprises multiple-type interlinked objects with various types of relationship which are becoming increasingly most popular named bibliographic networks, social media networks as well including the typical relational database data. In HIN, there are various data objects are interconnected through various relations. The entity linkage determines the corresponding entities from unstructured web text, in the existing HIN. This work is the most important and it is the most challenge because of ambiguity and existing limited knowledge. Some HIN could be considered as a domain-specific KB. The current Entity Linking (EL) systems aimed towards corpora which contain heterogeneous as web information and it performs sub-optimally on the domain-specific corpora. The EL systems used one or more general or specific domains of linking such as DBpedia, Wikipedia, Freebase, IMDB, YAGO, Wordnet and MKB. This paper presents a survey on domain-specific entity linking with HIN. This survey describes with a deep understanding of HIN, which includes datasets,types and examples with related concepts.Povezivanje entiteta je zadatak izvlačenja podataka koji povezuju spomenuti entitet u zbirci teksta sa njihovom sličnom bazom znanja, kao i zadatak dodjeljivanja jedinstvenog identiteta različitim entitetima, kao što su lokacije, pojedinci i tvrtke. Baza znanja (BZ) koristi se za optimizaciju prikupljanja, organizacije i pronalaženja informacija. Heterogene mreže informacija (HMI) obuhvaćaju višestruke međusobno povezane objekte različitih vrsta odnosa koji postaju sve popularniji i nazivaju se bibliografskim mrežama, mrežama društvenih medija, uključujući tipične podatke relacijske baze podataka. U HMI-u postoje razni podaci koji su međusobno povezani kroz različite odnose. Povezanost entiteta određuje odgovarajuće entitete iz nestrukturiranog teksta na webu u postojećem HMI-u. Ovaj je rad najvažniji i najveći izazov zbog nejasnoće i postojećeg ograničenog znanja. Neki se HMI mogu smatrati BZ-om specifičnim za domenu. Trenutni sustav povezivanja entiteta (PE) usmjeren je prema korpusima koji sadrže heterogene informacije kao web informacije i oni djeluju suptimalno na korpusima specifičnim za domenu. PE sustavi koristili su jednu ili više općih ili specifičnih domena povezivanja, kao što su DBpedia, Wikipedia, Freebase, IMDB, YAGO, Wordnet i MKB. U ovom radu predstavljeno je istraživanje o povezivanju entiteta specifičnog za domenu sa HMI-om. Ovo istraživanje opisuje s dubokim razumijevanjem HMI-a, što uključuje skupove podataka, vrste i primjere s povezanim konceptima

Spoken Term Detection on Low Resource Languages

Developing efficient speech processing systems for low-resource languages is an immensely challenging problem. One potentially effective approach to address the lack of resources for any particular language, is to employ data from multiple languages for building speech processing sub-systems. This thesis investigates possible methodologies for Spoken Term Detection (STD) from low- resource Indian languages. The task of STD intend to search for a query keyword, given in text form, from a considerably large speech database. This is usually done by matching templates of feature vectors, representing sequence of phonemes from the query word and the continuous speech from the database. Typical set of features used to represent speech signals in most of the speech processing systems are the mel frequency cepstral coefficients (MFCC). As speech is a very complexsignal, holding information about the textual message, speaker identity, emotional and health state of the speaker, etc., the MFCC features derived from it will also contain information about all these factors. For eficient template matching, we need to neutralize the speaker variability in features and stabilize them to represent the speech variability alone