Statistical Aspects of Music Mining: Naive Dictionary Representation

Extensive studies have been conducted on both musical scores and audio tracks of western classical music with the finality of learning and detecting the key in which a particular piece of music was played. Both the Bayesian Approach and modern unsupervised learning via latent Dirichlet allocation have been used for such learning tasks. In this research work, we propose and develop the novel idea of treating musical sheets as literary documents in the traditional text analytics parlance, to fully benefit from the vast amount of research already existing in statistical text mining and topic modeling. We specifically introduce the idea of representing any given piece of music as a collection of musical words that we codenamed muselets , which are essentially musical words of various lengths. Given the novelty and therefore the extremely difficulty of properly forming a complete version of a dictionary of muselets, the present paper focuses on a simpler albeit naive version of the ultimate dictionary, which we refer to as a Naive Dictionary because of the fact that all the words are of the same length. We specifically herein construct a naive dictionary featuring a corpus made up of African American, Chinese, Japanese and Arabic music, on which we perform both supervised and unsupervised learning. For the exploration of pattern recognition and topic modeling, we venture out of the traditional western classical music and embrace and explore other music genres. We consider the musical score sheets and audio tracks of some of the giants of jazz like Duke Ellington, Miles Davis, John Coltrane, Dizzie Gillespie, Wes Montgomery, Charlie Parker, Sonny Rollins, Louis Armstrong, Bill Evans, Dave Brubeck, Thelonious Monk. We specifically employ Bayesian techniques and modern topic modeling methods to explore tasks such as: automatic improvisation detection, genre identification, and key detection. Although some of the results based on the Naive Dictionary are reasonably good, we anticipate phenomenal predictive performances once we get around to actually build a full scale complete version of our intended dictionary of muselets

Arabic Handwritten Words Off-line Recognition based on HMMs and DBNs

International audienceIn this work, we investigate the combination of PGM (Propabilistic Graphical Models) classifiers, either independent or coupled, for the recognition of Arabic handwritten words. The independent classifiers are vertical and horizontal HMMs (Hidden Markov Models) whose observable outputs are features extracted from the image columns and the image rows respectively. The coupled classifiers associate the vertical and horizontal observation streams into a single DBN (Dynamic Bayesian Network). A novel method to extract word baseline and a simple and easily extractable features to construct feature vectors for words in the vocabulary are proposed. Some of these features are statistical, based on pixel distributions and local pixel configurations. Others are structural, based on the presence of ascenders, descenders, loops and diacritic points. Experiments on handwritten Arabic words from IFN/ENIT strongly support the feasibility of the proposed approach. The recognition rates achieve 90.42% with vertical and horizontal HMM, 85.03% and 85.21% with respectively a first and a second DBN which outperform results of some works based on PGMs

Advances in Document Layout Analysis

[EN] Handwritten Text Segmentation (HTS) is a task within the Document Layout Analysis field that aims to detect and extract the different page regions of interest found in handwritten documents. HTS remains an active topic, that has gained importance with the years, due to the increasing demand to provide textual access to the myriads of handwritten document collections held by archives and libraries. This thesis considers HTS as a task that must be tackled in two specialized phases: detection and extraction. We see the detection phase fundamentally as a recognition problem that yields the vertical positions of each region of interest as a by-product. The extraction phase consists in calculating the best contour coordinates of the region using the position information provided by the detection phase. Our proposed detection approach allows us to attack both higher level regions: paragraphs, diagrams, etc., and lower level regions like text lines. In the case of text line detection we model the problem to ensure that the system's yielded vertical position approximates the fictitious line that connects the lower part of the grapheme bodies in a text line, commonly known as the baseline. One of the main contributions of this thesis, is that the proposed modelling approach allows us to include prior information regarding the layout of the documents being processed. This is performed via a Vertical Layout Model (VLM). We develop a Hidden Markov Model (HMM) based framework to tackle both region detection and classification as an integrated task and study the performance and ease of use of the proposed approach in many corpora. We review the modelling simplicity of our approach to process regions at different levels of information: text lines, paragraphs, titles, etc. We study the impact of adding deterministic and/or probabilistic prior information and restrictions via the VLM that our approach provides. Having a separate phase that accurately yields the detection position (base- lines in the case of text lines) of each region greatly simplifies the problem that must be tackled during the extraction phase. In this thesis we propose to use a distance map that takes into consideration the grey-scale information in the image. This allows us to yield extraction frontiers which are equidistant to the adjacent text regions. We study how our approach escalates its accuracy proportionally to the quality of the provided detection vertical position. Our extraction approach gives near perfect results when human reviewed baselines are provided.[ES] La Segmentación de Texto Manuscrito (STM) es una tarea dentro del campo de investigación de Análisis de Estructura de Documentos (AED) que tiene como objetivo detectar y extraer las diferentes regiones de interés de las páginas que se encuentran en documentos manuscritos. La STM es un tema de investigación activo que ha ganado importancia con los años debido a la creciente demanda de proporcionar acceso textual a las miles de colecciones de documentos manuscritos que se conservan en archivos y bibliotecas. Esta tesis entiende la STM como una tarea que debe ser abordada en dos fases especializadas: detección y extracción. Consideramos que la fase de detección es, fundamentalmente, un problema de clasificación cuyo subproducto son las posiciones verticales de cada región de interés. Por su parte, la fase de extracción consiste en calcular las mejores coordenadas de contorno de la región utilizando la información de posición proporcionada por la fase de detección. Nuestro enfoque de detección nos permite atacar tanto regiones de alto nivel (párrafos, diagramas¿) como regiones de nivel bajo (líneas de texto principalmente). En el caso de la detección de líneas de texto, modelamos el problema para asegurar que la posición vertical estimada por el sistema se aproxime a la línea ficticia que conecta la parte inferior de los cuerpos de los grafemas en una línea de texto, comúnmente conocida como línea base. Una de las principales aportaciones de esta tesis es que el enfoque de modelización propuesto nos permite incluir información conocida a priori sobre la disposición de los documentos que se están procesando. Esto se realiza mediante un Modelo de Estructura Vertical (MEV). Desarrollamos un marco de trabajo basado en los Modelos Ocultos de Markov (MOM) para abordar tanto la detección de regiones como su clasificación de forma integrada, así como para estudiar el rendimiento y la facilidad de uso del enfoque propuesto en numerosos corpus. Así mismo, revisamos la simplicidad del modelado de nuestro enfoque para procesar regiones en diferentes niveles de información: líneas de texto, párrafos, títulos, etc. Finalmente, estudiamos el impacto de añadir información y restricciones previas deterministas o probabilistas a través de el MEV propuesto que nuestro enfoque proporciona. Disponer de un método independiente que obtiene con precisión la posición de cada región detectada (líneas base en el caso de las líneas de texto) simplifica enormemente el problema que debe abordarse durante la fase de extracción. En esta tesis proponemos utilizar un mapa de distancias que tiene en cuenta la información de escala de grises de la imagen. Esto nos permite obtener fronteras de extracción que son equidistantes a las regiones de texto adyacentes. Estudiamos como nuestro enfoque aumenta su precisión de manera proporcional a la calidad de la detección y descubrimos que da resultados casi perfectos cuando se le proporcionan líneas de base revisadas por humanos.[CA] La Segmentació de Text Manuscrit (STM) és una tasca dins del camp d'investigació d'Anàlisi d'Estructura de Documents (AED) que té com a objectiu detectar I extraure les diferents regions d'interès de les pàgines que es troben en documents manuscrits. La STM és un tema d'investigació actiu que ha guanyat importància amb els anys a causa de la creixent demanda per proporcionar accés textual als milers de col·leccions de documents manuscrits que es conserven en arxius i biblioteques. Aquesta tesi entén la STM com una tasca que ha de ser abordada en dues fases especialitzades: detecció i extracció. Considerem que la fase de detecció és, fonamentalment, un problema de classificació el subproducte de la qual són les posicions verticals de cada regió d'interès. Per la seva part, la fase d'extracció consisteix a calcular les millors coordenades de contorn de la regió utilitzant la informació de posició proporcionada per la fase de detecció. El nostre enfocament de detecció ens permet atacar tant regions d'alt nivell (paràgrafs, diagrames ...) com regions de nivell baix (línies de text principalment). En el cas de la detecció de línies de text, modelem el problema per a assegurar que la posició vertical estimada pel sistema s'aproximi a la línia fictícia que connecta la part inferior dels cossos dels grafemes en una línia de text, comunament coneguda com a línia base. Una de les principals aportacions d'aquesta tesi és que l'enfocament de modelització proposat ens permet incloure informació coneguda a priori sobre la disposició dels documents que s'estan processant. Això es realitza mitjançant un Model d'Estructura Vertical (MEV). Desenvolupem un marc de treball basat en els Models Ocults de Markov (MOM) per a abordar tant la detecció de regions com la seva classificació de forma integrada, així com per a estudiar el rendiment i la facilitat d'ús de l'enfocament proposat en nombrosos corpus. Així mateix, revisem la simplicitat del modelatge del nostre enfocament per a processar regions en diferents nivells d'informació: línies de text, paràgrafs, títols, etc. Finalment, estudiem l'impacte d'afegir informació i restriccions prèvies deterministes o probabilistes a través del MEV que el nostre mètode proporciona. Disposar d'un mètode independent que obté amb precisió la posició de cada regió detectada (línies base en el cas de les línies de text) simplifica enormement el problema que ha d'abordar-se durant la fase d'extracció. En aquesta tesi proposem utilitzar un mapa de distàncies que té en compte la informació d'escala de grisos de la imatge. Això ens permet obtenir fronteres d'extracció que són equidistants de les regions de text adjacents. Estudiem com el nostre enfocament augmenta la seva precisió de manera proporcional a la qualitat de la detecció i descobrim que dona resultats quasi perfectes quan se li proporcionen línies de base revisades per humans.Bosch Campos, V. (2020). Advances in Document Layout Analysis [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/138397TESI

Preprocessing Algorithm for Deciphering Historical Inscriptions Using String Metric

The article presents the improvements in the preprocessing part of the deciphering method (shortly preprocessing algorithm) for historical inscriptions of unknown origin. Glyphs used in historical inscriptions changed through time; therefore, various versions of the same script may contain different glyphs for each grapheme. The purpose of the preprocessing algorithm is reducing the running time of the deciphering process by filtering out the less probable interpretations of the examined inscription. However, the first version of the preprocessing algorithm leads incorrect outcome or no result in the output in certain cases. Therefore, its improved version was developed to find the most similar words in the dictionary by relaying the search conditions more accurately, but still computationally effectively. Moreover, a sophisticated similarity metric used to determine the possible meaning of the unknown inscription is introduced. The results of the evaluations are also detailed

Character-Aware Neural Language Models

We describe a simple neural language model that relies only on character-level inputs. Predictions are still made at the word-level. Our model employs a convolutional neural network (CNN) and a highway network over characters, whose output is given to a long short-term memory (LSTM) recurrent neural network language model (RNN-LM). On the English Penn Treebank the model is on par with the existing state-of-the-art despite having 60% fewer parameters. On languages with rich morphology (Arabic, Czech, French, German, Spanish, Russian), the model outperforms word-level/morpheme-level LSTM baselines, again with fewer parameters. The results suggest that on many languages, character inputs are sufficient for language modeling. Analysis of word representations obtained from the character composition part of the model reveals that the model is able to encode, from characters only, both semantic and orthographic information.Comment: AAAI 201

Ensemble learning using multi-objective optimisation for arabic handwritten words

Arabic handwriting recognition is a dynamic and stimulating field of study within pattern recognition. This system plays quite a significant part in today's global environment. It is a widespread and computationally costly function due to cursive writing, a massive number of words, and writing style. Based on the literature, the existing features lack data supportive techniques and building geometric features. Most ensemble learning approaches are based on the assumption of linear combination, which is not valid due to differences in data types. Also, the existing approaches of classifier generation do not support decision-making for selecting the most suitable classifier, and it requires enabling multi-objective optimisation to handle these differences in data types. In this thesis, new type of feature for handwriting using Segments Interpolation (SI) to find the best fitting line in each of the windows with a model for finding the best operating point window size for SI features. Multi-Objective Ensemble Oriented (MOEO) formulated to control the classifier topology and provide feedback support for changing the classifiers' topology and weights based on the extension of Non-dominated Sorting Genetic Algorithm (NSGA-II). It is designated as the Random Subset based Parents Selection (RSPS-NSGA-II) to handle neurons and accuracy. Evaluation metrics from two perspectives classification and Multiobjective optimization. The experimental design based on two subsets of the IFN/ENIT database. The first one consists of 10 classes (C10) and 22 classes (C22). The features were tested with Support Vector Machine (SVM) and Extreme Learning Machine (ELM). This work improved due to the SI feature. SI shows a significant result with SVM with 88.53% for C22. RSPS for C10 at k=2 achieved 91% accuracy with fewer neurons than NSGA-II, and for C22 at k=10, accuracy has been increased 81% compared to NSGA-II 78%. Future work may consider introducing more features to the system, applying them to other languages, and integrating it with sequence learning for more accuracy

Recognition of handwritten Arabic characters

The subject of handwritten character recognition has been receiving considerable attention in recent years due to the increased dependence on computers. Several methods for recognizing Latin, Chinese as well as Kanji characters have been proposed. However, work on recognition of Arabic characters has been relatively sparse. Techniques developed for recognizing characters in other languages can not be used for Arabic since the nature of Arabic characters is different. The shape of a character is a function of its location within a word where each character can have two to four different forms. Most of the techniques proposed to date for recognizing Arabic characters have relied on structural and topographic approaches. This thesis introduces a decision-theoretic approach to solve the problem. The proposed method involves, as a first step, digitization of the segmented character. The secondary part of the character (dots and zigzags) are then isolated and identified separately thereby reducing the recognition issue to a 20 class problem or less for each of the character forms. The moments of the horizontal and vertical projections of the remaining primary characters are calculated and normalized with respect to the zero order moment. Simple measures of shape are obtained from the normalized moments and incorporated into a feature vector. Classification is accomplished using quadratic discriminant functions. The approach was evaluated using isolated, handwritten characters from a data base established for this purpose. The classification rates varied from 97.5% to 100% depending on the form of the characters. These results indicate that the technique offers significantly better classification rates in comparison with existing methods

Advanced document data extraction techniques to improve supply chain performance

In this thesis, a novel machine learning technique to extract text-based information from scanned images has been developed. This information extraction is performed in the context of scanned invoices and bills used in financial transactions. These financial transactions contain a considerable amount of data that must be extracted, refined, and stored digitally before it can be used for analysis. Converting this data into a digital format is often a time-consuming process. Automation and data optimisation show promise as methods for reducing the time required and the cost of Supply Chain Management (SCM) processes, especially Supplier Invoice Management (SIM), Financial Supply Chain Management (FSCM) and Supply Chain procurement processes. This thesis uses a cross-disciplinary approach involving Computer Science and Operational Management to explore the benefit of automated invoice data extraction in business and its impact on SCM. The study adopts a multimethod approach based on empirical research, surveys, and interviews performed on selected companies.The expert system developed in this thesis focuses on two distinct areas of research: Text/Object Detection and Text Extraction. For Text/Object Detection, the Faster R-CNN model was analysed. While this model yields outstanding results in terms of object detection, it is limited by poor performance when image quality is low. The Generative Adversarial Network (GAN) model is proposed in response to this limitation. The GAN model is a generator network that is implemented with the help of the Faster R-CNN model and a discriminator that relies on PatchGAN. The output of the GAN model is text data with bonding boxes. For text extraction from the bounding box, a novel data extraction framework consisting of various processes including XML processing in case of existing OCR engine, bounding box pre-processing, text clean up, OCR error correction, spell check, type check, pattern-based matching, and finally, a learning mechanism for automatizing future data extraction was designed. Whichever fields the system can extract successfully are provided in key-value format.The efficiency of the proposed system was validated using existing datasets such as SROIE and VATI. Real-time data was validated using invoices that were collected by two companies that provide invoice automation services in various countries. Currently, these scanned invoices are sent to an OCR system such as OmniPage, Tesseract, or ABBYY FRE to extract text blocks and later, a rule-based engine is used to extract relevant data. While the system’s methodology is robust, the companies surveyed were not satisfied with its accuracy. Thus, they sought out new, optimized solutions. To confirm the results, the engines were used to return XML-based files with text and metadata identified. The output XML data was then fed into this new system for information extraction. This system uses the existing OCR engine and a novel, self-adaptive, learning-based OCR engine. This new engine is based on the GAN model for better text identification. Experiments were conducted on various invoice formats to further test and refine its extraction capabilities. For cost optimisation and the analysis of spend classification, additional data were provided by another company in London that holds expertise in reducing their clients' procurement costs. This data was fed into our system to get a deeper level of spend classification and categorisation. This helped the company to reduce its reliance on human effort and allowed for greater efficiency in comparison with the process of performing similar tasks manually using excel sheets and Business Intelligence (BI) tools.The intention behind the development of this novel methodology was twofold. First, to test and develop a novel solution that does not depend on any specific OCR technology. Second, to increase the information extraction accuracy factor over that of existing methodologies. Finally, it evaluates the real-world need for the system and the impact it would have on SCM. This newly developed method is generic and can extract text from any given invoice, making it a valuable tool for optimizing SCM. In addition, the system uses a template-matching approach to ensure the quality of the extracted information