A Review on Improve Handwritten character recognition by using Convolutional Neural Network

For image recognition CNN is the most popular learning model. The features like weight sharing strategy and strong relations of the pixels of the image makes CNN best choice for image recognition. The feature extraction and classification can be done simultaneously in deep learning models which has proved very needful compared to the traditional methods. A promising recognition can be obtained by using CNN if we address to certain issues. So in CNN based framework for handwritten character recognition that gives a better performance compared to other CNN based recognition methods

Study and Observation of the Variations of Accuracies for Handwritten Digits Recognition with Various Hidden Layers and Epochs using Neural Network Algorithm

In recent days, Artificial Neural Network (ANN) can be applied to a vast majority of fields including business, medicine, engineering, etc. The most popular areas where ANN is employed nowadays are pattern and sequence recognition, novelty detection, character recognition, regression analysis, speech recognition, image compression, stock market prediction, Electronic nose, security, loan applications, data processing, robotics, and control. The benefits associated with its broad applications leads to increasing popularity of ANN in the era of 21st Century. ANN confers many benefits such as organic learning, nonlinear data processing, fault tolerance, and self-repairing compared to other conventional approaches. The primary objective of this paper is to analyze the influence of the hidden layers of a neural network over the overall performance of the network. To demonstrate this influence, we applied neural network with different layers on the MNIST dataset. Also, another goal is to observe the variations of accuracies of ANN for different numbers of hidden layers and epochs and to compare and contrast among them.Comment: To be published in the 4th IEEE International Conference on Electrical Engineering and Information & Communication Technology (iCEEiCT 2018

Obtaining n best alternatives for classifying Unicode symbols

The Unicode character set has been increased in last years until grouping more than 100000 characters. We developed a classifier which can predict the n most probable solutions to a given handwritten character in a smaller Unicode set. Even with the size reduction we still have a classification problem with a big number of classes (5488 in total) without any training sample. Before dealing with this problem we performed some experiments on the UJI PEN dataset. In these experiments we used two different data generation techniques, distortions and variational autoencoders as generative models. We tried feature extraction methods with both offline and online data. The generation along with the feature extraction was tested in several models of neural networks like convolutional networks or LSTM.El conjunto de caracteres Unicode se ha incrementado en los últimos años hasta llegar a agrupar más de 100000 caracteres. Hemos desarrollado un clasificador que puede predecir las n clases más probables de un carácter escrito a mano perteneciente a un conjunto más pequeño de caracteres Unicode. Incluso con la reducción de tamaño todavía tenemos un problema de clasificación con muchas clases (5488 en total) sin ninguna muestra de entrenamiento. Antes de tratar este problema hemos realizado algunos experimentos con el corpus UJI PEN. En estos experimentos hemos utilizado dos técnicas de generación de datos, distorsiones y el uso devariational autoencoders como modelos generativos. Hemos probado diferentes métodos de extracción de características tanto con datos offline como con datos online. La generación y la extracción de características han sido probadas en diferentes modelos de redes neuronales como las redes convolucionales o las LSTM.Vieco Pérez, J. (2017). Obtención de las n mejores alternativas para clasificación de símbolos unicode. http://hdl.handle.net/10251/86238TFG

A Multi-Feature Selection Approach for Gender Identification of Handwriting based on Kernel Mutual Information

This paper presents a new flexible approach to predict the gender of the writers from their handwriting samples. Handwriting features like slant, curvature, line separation, chain code, character shapes, and more, can be extracted from different methods. Therefore, the multi-feature sets are irrelevant and redundant. The conflict of the features exists in the sets, which affects the accuracy of classification and the computing cost. This paper proposes an approach, named Kernel Mutual Information (KMI), that focuses on feature selection. The KMI approach can decrease redundancies and conflicts. In addition, it extracts an optimal subset of features from the writing samples produced by male and female writers. To ensure that KMI can apply the various features, this paper describes the handwriting segmentation and handwritten text recognition technology used. The classification is carried out using a Support Vector Machine (SVM) on two databases. The first database comes from the ICDAR 2013 competition on gender prediction, which provides the samples in both Arabic and English. The other database contains the Registration-Document-Form (RDF) database in Chinese. The proposed and compared methods were evaluated on both databases. Results from the methods highlight the importance of feature selection for gender prediction from handwriting

An Unsupervised Classification Technique for Detection of Flipped Orientations in Document Images

Detection of text orientation in document images is of preliminary concern prior to processing of documents by Optical Character Reader. The text direction in document images should exist generally in a specific orientation, i.e.,   text direction for any automated document reading system. The flipped text orientation leads to an unambiguous result in such fully automated systems. In this paper, we focus on development of text orientation direction detection module which can be incorporated as the perquisite process in automatic reading system. Orientation direction detection of text is performed through employing directional gradient features of document image and adapts an unsupervised learning approach for detection of flipped text orientation at which the document has been originally fed into scanning device. The unsupervised learning is built on the directional gradient features of text of document based on four possible different orientations. The algorithm is experimented on document samples of printed plain English text as well as filled in pre-printed forms of Telugu script. The outcome attained by algorithm proves to be consistent and adequate with an average accuracy around 94%

Histograms of Points, Orientations, and Dynamics of Orientations Features for Hindi Online Handwritten Character Recognition

A set of features independent of character stroke direction and order variations is proposed for online handwritten character recognition. A method is developed that maps features like co-ordinates of points, orientations of strokes at points, and dynamics of orientations of strokes at points spatially as a function of co-ordinate values of the points and computes histograms of these features from different regions in the spatial map. Different features like spatio-temporal, discrete Fourier transform, discrete cosine transform, discrete wavelet transform, spatial, and histograms of oriented gradients used in other studies for training classifiers for character recognition are considered. The classifier chosen for classification performance comparison, when trained with different features, is support vector machines (SVM). The character datasets used for training and testing the classifiers consist of online handwritten samples of 96 different Hindi characters. There are 12832 and 2821 samples in training and testing datasets, respectively. SVM classifiers trained with the proposed features has the highest classification accuracy of 92.9\% when compared to the performances of SVM classifiers trained with the other features and tested on the same testing dataset. Therefore, the proposed features have better character discriminative capability than the other features considered for comparison.Comment: 21 pages, 12 jpg figure