25 research outputs found

    Handwritten Digit Recognition by Fourier-Packet Descriptors

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    Any statistical pattern recognition system includes a feature extraction component. For character patterns, several feature families have been tested, such as the Fourier-Wavelet Descriptors. We are proposing here a generalization of this family: the Fourier-Packet Descriptors. We have selected sets of these features and tested them on handwritten digits: the error rate was 1.55% with a polynomial classifier for a 70 features set and 1.97% with a discriminative learning quadratic discriminant function for a 40 features set

    Novel Dynamic Structure Neural Network for Optical Character Recognition

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    [[abstract]]This paper presents a novel dynamic structure neural network (DSNN) and a learning algorithm for training DSNN. The performance of a neural network system depends on several factors. In that, the architecture of a neural network plays an important role. The objective of the developing DSNN is to avoid trial-and-error process for designing a neural network system. The architecture of DSNN consists of a three-dimensional set of neurons with input/output nodes and connection weights. Designers can define the maximum connection number of each neuron. Moreover, designers can manually deploy neurons in a virtual 3D space, or randomly generate the system structure by the proposed learning algorithm. This work also develops an automatic restructuring algorithm integrated in the proposed learning algorithm to improve the system performance. Due to the novel dynamic structure of DSNN and the restructuring algorithm, the design of DSNN is fast and convenient. Furthermore, DSNN is implemented in C++ with man-machine interactive procedures and tested on many cases with very promising results.[[conferencetype]]國際[[conferencedate]]20041218~20041221[[iscallforpapers]]Y[[conferencelocation]]Rome, Ital

    Feature Extraction Methods for Character Recognition

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    Modelos conexionistas auto-organizados y su aplicación en reconocimiento de patrones

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    El trabajo en curso tiene por objeto desarrollar técnicas conexionistas para reconocimiento de patrones. A partir del sistema ya desarrollado por el grupo (que consiste básicamente en un modelo híbrido no supervisado -de tipo autoorganizado- seguido de una instancia supervisada) se estudia la introducción de innovaciones que incrementen su potencia y su eficiencia. Las investigaciones en curso giran alrededor de dos frentes: i) preprocesamiento de la entrada: se consideran opciones a la técnica inicialmente empleada (máscaras de Kirsch), entre ellas el uso de transformadas wavelet y la extracción de componentes principales; ii) estructura del módulo intermedio (no supervisado): analizamos posibles sofisticaciones orientadas a obtener una clasificación más especializada de acuerdo con las características de la distribución de los datos de entrada. La calidad y eficiencia de la propuesta resultante deberán ser luego comparadas con las de los métodos ya existentes.Eje: Inteligencia artificialRed de Universidades con Carreras en Informática (RedUNCI

    Modelos conexionistas auto-organizados y su aplicación en reconocimiento de patrones

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    El trabajo en curso tiene por objeto desarrollar técnicas conexionistas para reconocimiento de patrones. A partir del sistema ya desarrollado por el grupo (que consiste básicamente en un modelo híbrido no supervisado -de tipo autoorganizado- seguido de una instancia supervisada) se estudia la introducción de innovaciones que incrementen su potencia y su eficiencia. Las investigaciones en curso giran alrededor de dos frentes: i) preprocesamiento de la entrada: se consideran opciones a la técnica inicialmente empleada (máscaras de Kirsch), entre ellas el uso de transformadas wavelet y la extracción de componentes principales; ii) estructura del módulo intermedio (no supervisado): analizamos posibles sofisticaciones orientadas a obtener una clasificación más especializada de acuerdo con las características de la distribución de los datos de entrada. La calidad y eficiencia de la propuesta resultante deberán ser luego comparadas con las de los métodos ya existentes.Eje: Inteligencia artificialRed de Universidades con Carreras en Informática (RedUNCI

    Development of Features for Recognition of Handwritten Odia Characters

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    In this thesis, we propose four different schemes for recognition of handwritten atomic Odia characters which includes forty seven alphabets and ten numerals. Odia is the mother tongue of the state of Odisha in the republic of India. Optical character recognition (OCR) for many languages is quite matured and OCR systems are already available in industry standard but, for the Odia language OCR is still a challenging task. Further, the features described for other languages can’t be directly utilized for Odia character recognition for both printed and handwritten text. Thus, the prime thrust has been made to propose features and utilize a classifier to derive a significant recognition accuracy. Due to the non-availability of a handwritten Odia database for validation of the proposed schemes, we have collected samples from individuals to generate a database of large size through a digital note maker. The database consists of a total samples of 17, 100 (150 × 2 × 57) collected from 150 individuals at two different times for 57 characters. This database has been named Odia handwritten character set version 1.0 (OHCS v1.0) and is made available in http://nitrkl.ac.in/Academic/Academic_Centers/Centre_For_Computer_Vision.aspx for the use of researchers. The first scheme divides the contour of each character into thirty segments. Taking the centroid of the character as base point, three primary features length, angle, and chord-to-arc-ratio are extracted from each segment. Thus, there are 30 feature values for each primary attribute and a total of 90 feature points. A back propagation neural network has been employed for the recognition and performance comparisons are made with competent schemes. The second contribution falls in the line of feature reduction of the primary features derived in the earlier contribution. A fuzzy inference system has been employed to generate an aggregated feature vector of size 30 from 90 feature points which represent the most significant features for each character. For recognition, a six-state hidden Markov model (HMM) is employed for each character and as a consequence we have fifty-seven ergodic HMMs with six-states each. An accuracy of 84.5% has been achieved on our dataset. The third contribution involves selection of evidence which are the most informative local shape contour features. A dedicated distance metric namely, far_count is used in computation of the information gain values for possible segments of different lengths that are extracted from whole shape contour of a character. The segment, with highest information gain value is treated as the evidence and mapped to the corresponding class. An evidence dictionary is developed out of these evidence from all classes of characters and is used for testing purpose. An overall testing accuracy rate of 88% is obtained. The final contribution deals with the development of a hybrid feature derived from discrete wavelet transform (DWT) and discrete cosine transform (DCT). Experimentally it has been observed that a 3-level DWT decomposition with 72 DCT coefficients from each high-frequency components as features gives a testing accuracy of 86% in a neural classifier. The suggested features are studied in isolation and extensive simulations has been carried out along with other existing schemes using the same data set. Further, to study generalization behavior of proposed schemes, they are applied on English and Bangla handwritten datasets. The performance parameters like recognition rate and misclassification rate are computed and compared. Further, as we progress from one contribution to the other, the proposed scheme is compared with the earlier proposed schemes

    Empirical mode decomposition-based facial pose estimation inside video sequences

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    We describe a new pose-estimation algorithm via integration of the strength in both empirical mode decomposition (EMD) and mutual information. While mutual information is exploited to measure the similarity between facial images to estimate poses, EMD is exploited to decompose input facial images into a number of intrinsic mode function (IMF) components, which redistribute the effect of noise, expression changes, and illumination variations as such that, when the input facial image is described by the selected IMF components, all the negative effects can be minimized. Extensive experiments were carried out in comparisons to existing representative techniques, and the results show that the proposed algorithm achieves better pose-estimation performances with robustness to noise corruption, illumination variation, and facial expressions

    Fast algorithms for wavelet-based analysis of hyperspectral signatures

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    Hyperspectral sensors promise great improvements in the quality of information gathered for remote sensing applications. However, they also present a huge challenge to data storage and computing systems. Thus there is a great need for reliable compression schemes, as well as analysis tools that can exploit the hyperspectral data in a computationally efficient manner. It has been proposed that wavelet-based methods may be superior to currently used methods for the analysis of hyperspectral signatures. In this thesis, a wavelet-based method, as well as traditional analytical methods, was implemented and applied to hyperspectral images. The computational expense of the various methods are determined analytically and experimentally to show advantages of the wavelet-based methods. Various measures, including cross correlation, signal-to-noise ratios and Euclidean distance, are designed and implemented for comparing the differences that might exist between the outputs of the algorithms
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