1,279 research outputs found
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
Capsule-Based Persian/Arabic Robust Handwritten Digit Recognition Using EM Routing
In this paper, the problem of handwritten digit recognition has been
addressed. However, the underlying language is Persian/Arabic, and the system
with which this task is a capsule network (CapsNet) has recently emerged as a
more advanced architecture than its ancestor, namely CNN (Convolutional Neural
Network). The training of the architecture is performed using the Hoda dataset,
which has been provided for Persian/Arabic handwritten digits. The output of
the system clearly outperforms the results achieved by its ancestors, as well
as other previously presented recognition algorithms.Comment: 5 pages, 10 figures, 4th International Conference on Pattern
Recognition and Image Analysis (IPRIA2019), IEE
A shape descriptor based on trainable COSFIRE filters for the recognition of handwritten digits
The recognition of handwritten digits is an application which
has been used as a benchmark for comparing shape recognition methods.
We train COSFIRE filters to be selective for different parts of handwritten
digits. In analogy with the neurophysiological concept of population
coding we use the responses of multiple COSFIRE filters as a shape descriptor
of a handwritten digit. We demonstrate the effectiveness of the
proposed approach on two data sets of handwritten digits: Western Arabic
(MNIST) and Farsi for which we achieve high recognition rates of
99.52% and 99.33%, respectively. COSFIRE filters are conceptually simple,
easy to implement and they are versatile trainable feature detectors.
The shape descriptor that we propose is highly effective to the automatic
recognition of handwritten digits.peer-reviewe
Handwritten Arabic Digit Recognition Using Convolutional Neural Network
In Computer vision systems, computer vision works by imitating humans in their vision way which is known as the human vision system (HVS). In HVS, humans use their eyes and brains in order to see and classify any object around them. Hence, computer vision systems imitate HSV by developing several algorithms for classifying images and objects. The main goal of this paper is to propose a model for identifying and classifying the Arabic handwritten digits with high accuracy. The concept of deep learning via the convolutional neural network (CNN) with the ADBase database is used to achieve the goal. The training is done by having a 3*3 and 5*5 filters. Basically, while the classification phase distinct learning rates are used to train the network. The obtained results are encouraging and promising
Applying Data Augmentation to Handwritten Arabic Numeral Recognition Using Deep Learning Neural Networks
Handwritten character recognition has been the center of research and a
benchmark problem in the sector of pattern recognition and artificial
intelligence, and it continues to be a challenging research topic. Due to its
enormous application many works have been done in this field focusing on
different languages. Arabic, being a diversified language has a huge scope of
research with potential challenges. A convolutional neural network model for
recognizing handwritten numerals in Arabic language is proposed in this paper,
where the dataset is subject to various augmentation in order to add robustness
needed for deep learning approach. The proposed method is empowered by the
presence of dropout regularization to do away with the problem of data
overfitting. Moreover, suitable change is introduced in activation function to
overcome the problem of vanishing gradient. With these modifications, the
proposed system achieves an accuracy of 99.4\% which performs better than every
previous work on the dataset.Comment: 5 pages, 6 figures, 3 table
Neural Networks for Handwritten English Alphabet Recognition
This paper demonstrates the use of neural networks for developing a system
that can recognize hand-written English alphabets. In this system, each English
alphabet is represented by binary values that are used as input to a simple
feature extraction system, whose output is fed to our neural network system.Comment: 5 pages, 3 Figure, ISSN:0975 - 888
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