An Efficient Hidden Markov Model for Offline Handwritten Numeral Recognition

Traditionally, the performance of ocr algorithms and systems is based on the recognition of isolated characters. When a system classifies an individual character, its output is typically a character label or a reject marker that corresponds to an unrecognized character. By comparing output labels with the correct labels, the number of correct recognition, substitution errors misrecognized characters, and rejects unrecognized characters are determined. Nowadays, although recognition of printed isolated characters is performed with high accuracy, recognition of handwritten characters still remains an open problem in the research arena. The ability to identify machine printed characters in an automated or a semi automated manner has obvious applications in numerous fields. Since creating an algorithm with a one hundred percent correct recognition rate is quite probably impossible in our world of noise and different font styles, it is important to design character recognition algorithms with these failures in mind so that when mistakes are inevitably made, they will at least be understandable and predictable to the person working with theComment: 6pages, 5 figure

Characters Segmentation of Cursive Handwritten Words based on Contour Analysis and Neural Network Validation

This paper presents a robust algorithm to identify the letter boundaries in images of unconstrained handwritten word . The proposed algorithm is based on  vertical  contour  analysis.  Proposed  algorithm  is  performed  to  generate  presegmentation by analyzing the vertical contours from right to left. The unwanted segmentation  points  are  reduced  using  neural  network  validation  to  improve accuracy  of  segmentation.  The  neural  network  is  utilized  to  validate segmentation  points.  The  experiments  are  performed  on  the  IAM  benchmark database.  The  results  are  showing  that  the  proposed  algorithm  capable  to accurately locating the letter boundaries for unconstrained handwritten words

Una aplicación móvil para el reconocimiento automático de matrículas de automóviles argentinos

Los sistemas de reconocimiento autom ático de matr culas o ANPR (del ingl es Automatic Number Plate Recognition) son una colecci ón de elementos de hardware y software que utilizan reconocimiento óptico de caracteres en im ágenes para identi ficar las matrí culas de los veh ículos. Si bien existen diversas implementaciones de estos sistemas, no se ha encontrado una aplicaci ón móovil de mano y de bajo costo que reconozca matrí culas argentinas para situaciones de control vehicular, por ejemplo, operativos de inspecci ón policial, identi ficacióon en puestos de peajes, con- trol de estacionamiento, entre otros. En este trabajo se desarroll o una aplicaci ón de c ódigo abierto para dispositivos m óviles inteligentes sobre plataforma Android, que consigue reconocer matr ículas argentinas de forma instant anea.Eje: Workshop Innovación en sistemas de software (WISS)Red de Universidades con Carreras en Informática (RedUNCI

User-independent accelerometer-based gesture recognition for mobile devices

Many mobile devices embed nowadays inertial sensors. This enables new forms of human-computer interaction through the use of gestures (movements performed with the mobile device) as a way of communication. This paper presents an accelerometer-based gesture recognition system for mobile devices which is able to recognize a collection of 10 different hand gestures. The system was conceived to be light and to operate in a user -independent manner in real time. The recognition system was implemented in a smart phone and evaluated through a collection of user tests, which showed a recognition accuracy similar to other state-of-the art techniques and a lower computational complexity. The system was also used to build a human -robot interface that enables controlling a wheeled robot with the gestures made with the mobile phone

Abordagens livres de segmentação para reconhecimento automático de cadeias numéricas manuscritas utilizando aprendizado profundo

Orientador: Prof Dr. Luiz Eduardo Soares de OliveiraCoorientador: Prof. Dr. Robert SabourinTese (doutorado) - Universidade Federal do Paraná, Setor de Ciências Exatas, Programa de Pós-Graduação em Tecnologia. Defesa : Curitiba, 12/03/2019Inclui referências: p.83-90Resumo: Nas ultimas decadas, o reconhecimento de cadeias numericas manuscritas tem sido abordado de maneira similar por varios autores, no que se refere ao tratamento de digitos conectados. A necessidade de segmentar esses componentes e um consenso. Dessa forma, as propostas se concentram em determinar os pontos de segmentacao aplicando heuristicas sobre caracteristicas extraidas do objeto, plano de fundo, contorno, entre outras. No entanto, a producao de digitos fragmentados, ocasionando a sobre-segmentacao da cadeia, e um problema comum entre essas abordagem. Assim, as metologias sao categorizadas pela forma como manipulam os componentes resultantes desse processo: (a) Naquelas que produzem apenas uma segmentacao possivel, ou (b) naquelas que definem um conjunto de hipoteses de segmentacao, alem de um metodo de fusao para determinar a hipotese mais provavel. Apesar da segunda categoria apresentar taxas de reconhecimento mais elevadas, o custo computacional torna-se um aspecto desfavoravel, devido as recorrentes consultas ao classificador pelas inumeras hipoteses produzidas. Alem disso, a variabilidade dos tipos de conexao entre os digitos e a falta de contexto, como a informacao sobre a quantidade de digitos, denotam a limitacao de abordagens baseadas em processos heuristicos. Visando evitar estes problemas, evidenciamos ser possivel superar os metodos tradicionais implementando modelos baseados em aprendizado profundo para classificar digitos conectados diretamente, reduzindo a etapa de segmentacao a um processo de deteccao de componente conexo. Alem disso, aproveitando os avancos na area de deteccao de objetos, apresentamos uma nova abordagem para o problema, na qual, digitos passam a ser compreendidos como objetos em uma imagem e neste cenario, uma sequencia de digitos e uma sequencia de objetos. Para validar nossas hipoteses, experimentos realizados em bases de conhecimento geral avaliaram nossas propostas com os trabalhos presentes na literatura em termos de reconhecimento, correta segmentacao e custo computacional. Os resultados atingiram taxas de reconhecimento em torno 97% quando aplicado a uma base de duplas de digitos conectados e 95% para as amostras de cadeias da base NIST SD19, superando os niveis do estado da arte. Alem das altas taxas de reconhecimento, tambem houve significativa reducao de consultas ao classificador (custo computacional), principalmente em casos complexos, superando o desempenho dos trabalhos presentes no estado da arte, denotando o potencial das abordagens propostas.Abstract: Over the last decades, the recognition of handwritten digit strings has been approached in a similar way by several authors, regarding the connected digits issue. The segmentation of these components is a consensus. In this way, the approaches attempt to determining the segmentation points by applying heuristics on features extracted from the object, background, contour, etc. However, the production of fragmented digits, causing the over-segmentation of the string is a common problem among these approaches. Thus, the methodologies are categorized by the way they manipulate the components resulting from this process: (a) those ones that produce only a possible segmentation, or (b) those ones that define a set of segmentation hypotheses and a fusion method to determine the best hypothesis. Although the second category has higher recognition rates, the computational cost becomes an unfavorable aspect, due to the recurrent classifier calls to classify the hypotheses produced. Therefore, the variability of the connection types and the lack of context, such as the number of digits present in the string, denote the limitation of approaches based on heuristic processes. In order to avoid these problems, we believe that is possible to overcome traditional methods by implementing models based on deep learning to classify connected digits directly, reducing the segmentation step to a connected component detection process. In addition, taking advantage of advances of object detection field, we propose a new approach to the problem, in which, digits are understood as objects in an image and in this scenario, a sequence of digits is a sequence of objects. To validate our hypotheses, experiments were carried out in well-known datasets, evaluating our proposals against state-of-art in terms of recognition, correct segmentation and computational cost. The results achieved recognition rates of 97% when applied to a base of connected digit pairs, and 95% for the NIST SD19 samples, surpassing state-of-art levels. Besides the high recognition rates, it has a significant reduction in terms of classifier calls (computational cost), especially in complex cases, surpassing the performance of the works present in the state of the art, denoting the potential of the proposed approaches

Adaptive systems for hidden Markov model-based pattern recognition systems

This thesis focuses on the design of adaptive systems (AS) for dealing with complex pattern recognition problems. Pattern recognition systems usually rely on static knowledge to define a configuration to be used during their entire lifespan. However, some systems need to adapt to knowledge that may not have been available in the design phase. For this reason, AS are designed to tailor a baseline pattern recognition system as required, and in an automated fashion, in both the learning and generalization phases. These AS are defined here, using hidden Markov model (HMM)-based classifiers as a case study. We first evaluate incremental learning algorithms for the estimation of HMM parameters. The main goal is to find incremental learning algorithms that perform as well as the traditional batch learning techniques, but incorporate the advantages of incremental learning for designing complex pattern recognition systems. Experiments on handwritten characters have shown that a proposed variant of the Ensemble Training algorithm, which employs ensembles of HMMs, can lead to very promising results. Furthermore, the use of a validation dataset demonstrates that it is possible to achieve better performances than those of batch learning. We then propose a new approach for the dynamic selection of ensembles of classifiers. Based on the concept called “multistage organizations”, the main objective of which is to define a multi-layer fusion function that adapts to individual recognition problems, we propose dynamic multistage organization (DMO), which defines the best multistage structure for each test sample. By extending Dos Santos et al’s approach, we propose two implementations for DMO, namely DSAm and DSAc. DSAm considers a set of dynamic selection functions to generalize a DMO structure, and DSAc uses contextual information, represented by the output profiles computed from the validation dataset. The experimental evaluation, considering both small and large datasets, demonstrates that DSAc outperforms DSAm on most problems. This shows that the use of contextual information can result in better performance than other methods. The performance of DSAc can also be enhanced in incremental learning. However, the most important observation, supported by additional experiments, is that dynamic selection is generally preferred over static approaches when the recognition problem presents a high level of uncertainty. Finally, we propose the LoGID (Local and Global Incremental Learning for Dynamic Selection) framework, the main goal of which is to adapt hidden Markov model-based pattern recognition systems in both the learning and generalization phases. Given that the baseline system is composed of a pool of base classifiers, adaptation during generalization is conducted by dynamically selecting the best members of this pool to recognize each test sample. Dynamic selection is performed by the proposed K-nearest output profiles algorithm, while adaptation during learning consists of gradually updating the knowledge embedded in the base classifiers by processing previously unobserved data. This phase employs two types of incremental learning: local and global. Local incremental learning involves updating the pool of base classifiers by adding new members to this set. These new members are created with the Learn++ algorithm. In contrast, global incremental learning consists of updating the set of output profiles used during generalization. The proposed framework has been evaluated on a diversified set of databases. The results indicate that LoGID is promising. In most databases, the recognition rates achieved by the proposed method are higher than those achieved by other state-of-the-art approaches, such as batch learning. Furthermore, the simulated incremental learning setting demonstrates that LoGID can effectively improve the performance of systems created with small training sets as more data are observed over time