Segmental K-Means Learning with Mixture Distribution for HMM Based Handwriting Recognition

This paper investigates the performance of hidden Markov models (HMMs) for handwriting recognition. The Segmental K-Means algorithm is used for updating the transition and observation probabilities, instead of the Baum-Welch algorithm. Observation probabilities are modelled as multi-variate Gaussian mixture distributions. A deterministic clustering technique is used to estimate the initial parameters of an HMM. Bayesian information criterion (BIC) is used to select the topology of the model. The wavelet transform is used to extract features from a grey-scale image, and avoids binarization of the image.</p

Symbolic and Deep Learning Based Data Representation Methods for Activity Recognition and Image Understanding at Pixel Level

Efficient representation of large amount of data particularly images and video helps in the analysis, processing and overall understanding of the data. In this work, we present two frameworks that encapsulate the information present in such data. At first, we present an automated symbolic framework to recognize particular activities in real time from videos. The framework uses regular expressions for symbolically representing (possibly infinite) sets of motion characteristics obtained from a video. It is a uniform framework that handles trajectory-based and periodic articulated activities and provides polynomial time graph algorithms for fast recognition. The regular expressions representing motion characteristics can either be provided manually or learnt automatically from positive and negative examples of strings (that describe dynamic behavior) using offline automata learning frameworks. Confidence measures are associated with recognitions using Levenshtein distance between a string representing a motion signature and the regular expression describing an activity. We have used our framework to recognize trajectory-based activities like vehicle turns (U-turns, left and right turns, and K-turns), vehicle start and stop, person running and walking, and periodic articulated activities like digging, waving, boxing, and clapping in videos from the VIRAT public dataset, the KTH dataset, and a set of videos obtained from YouTube. Next, we present a core sampling framework that is able to use activation maps from several layers of a Convolutional Neural Network (CNN) as features to another neural network using transfer learning to provide an understanding of an input image. The intermediate map responses of a Convolutional Neural Network (CNN) contain information about an image that can be used to extract contextual knowledge about it. Our framework creates a representation that combines features from the test data and the contextual knowledge gained from the responses of a pretrained network, processes it and feeds it to a separate Deep Belief Network. We use this representation to extract more information from an image at the pixel level, hence gaining understanding of the whole image. We experimentally demonstrate the usefulness of our framework using a pretrained VGG-16 model to perform segmentation on the BAERI dataset of Synthetic Aperture Radar (SAR) imagery and the CAMVID dataset. Using this framework, we also reconstruct images by removing noise from noisy character images. The reconstructed images are encoded using Quadtrees. Quadtrees can be an efficient representation in learning from sparse features. When we are dealing with handwritten character images, they are quite susceptible to noise. Hence, preprocessing stages to make the raw data cleaner can improve the efficacy of their use. We improve upon the efficiency of probabilistic quadtrees by using a pixel level classifier to extract the character pixels and remove noise from the images. The pixel level denoiser uses a pretrained CNN trained on a large image dataset and uses transfer learning to aid the reconstruction of characters. In this work, we primarily deal with classification of noisy characters and create the noisy versions of handwritten Bangla Numeral and Basic Character datasets and use them and the Noisy MNIST dataset to demonstrate the usefulness of our approach

Classification of individuals using handwritten numeral with geometric morphometric techniques

No individual will have exactly same handwriting. Handwriting style is something that will acquire and develop through years of learning involving questioned that form individuality. The analysis of handwriting for the purposes of author identification had started since the origin of the handwriting itself. It assisted investigator in crime involving document analysis such as fraud, document falsification and many more. The numbers that used nowadays are also called Hindu- Arabic numbers are combination of 10 digits which are 1,2,3,4,5,,6,7,8,9,and 0. It is a base ten system since the value increases by the power of ten. The handwriting style has very high variations sizes, shapes and fonts. However, the most distinctive characteristics is the shape itself. Geometric morphometric (GMM) is a study of variations in shape. It uses Cartesian geometric coordinates rather than linear, area or volumetric variables. The basis of the technique is coordinates of identifiable landmarks (LM) and semilandmarks (SL) to capture the image shape. In this project, 200 samples of consisting of numeral 1, 6, 8 and 9 were collected from 10 students of Universiti Sains Malaysia (USM). The number is based on previous study by Tay Eue Kam (2019), as it successfully used in ethnic discrimination by using fully manual method based on slant, width and height measurement. The PCA of PC1 and PC2 shows cumulative variance percentage for numeral 1, 6, 8 and 9 were 100.00%, 67.59%, 56.34% and 65.17% respectively. Numeral 6 was the only numerals that show significant differences between individuals. Geometric morphometric is useful in individual identification based on shape of the numerals

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

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

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

High-Quality Wavelets Features Extraction for Handwritten Arabic Numerals Recognition

Arabic handwritten digit recognition is the science of recognition and classification of handwritten Arabic digits. It has been a subject of research for many years with rich literature available on the subject.  Handwritten digits written by different people are not of the same size, thickness, style, position or orientation. Hence, many different challenges have to overcome for resolving the problem of handwritten digit recognition.  The variation in the digits is due to the writing styles of different people which can differ significantly.  Automatic handwritten digit recognition has wide application such as automatic processing of bank cheques, postal addresses, and tax forms. A typical handwritten digit recognition application consists of three main stages namely features extraction, features selection, and classification. One of the most important problems is feature extraction. In this paper, a novel feature extraction approach for off-line handwritten digit recognition is presented. Wavelets-based analysis of image data is carried out for feature extraction, and then classification is performed using various classifiers. To further reduce the size of training data-set, high entropy subbands are selected. To increase the recognition rate, individual subbands providing high classification accuracies are selected from the over-complete tree. The features extracted are also normalized to standardize the range of independent variables before providing them to the classifier. Classification is carried out using k-NN and SVMs. The results show that the quality of extracted features is high as almost equivalently high classification accuracies are acquired for both classifiers, i.e. k-NNs and SVMs