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Handwritten Arabic Numeral Recognition using a Multi Layer Perceptron
TL;DR: A feature set of 88 features is designed to represent samples of handwritten Arabic numerals designed to include 72 shadow and 16 octant features and can be extended to include OCR of handwritten characters of Arabic alphabet.
Abstract: Handwritten numeral recognition is in general a benchmark problem of Pattern Recognition and Artificial Intelligence Compared to the problem of printed numeral recognition, the problem of handwritten numeral recognition is compounded due to variations in shapes and sizes of handwritten characters Considering all these, the problem of handwritten numeral recognition is addressed under the present work in respect to handwritten Arabic numerals Arabic is spoken throughout the Arab World and the fifth most popular language in the world slightly before Portuguese and Bengali For the present work, we have developed a feature set of 88 features is designed to represent samples of handwritten Arabic numerals for this work It includes 72 shadow and 16 octant features A Multi Layer Perceptron (MLP) based classifier is used here for recognition handwritten Arabic digits represented with the said feature set On experimentation with a database of 3000 samples, the technique yields an average recognition rate of 9493% evaluated after three-fold cross validation of results It is useful for applications related to OCR of handwritten Arabic Digit and can also be extended to include OCR of handwritten characters of Arabic alphabet
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TL;DR: An automatic handwriting recognition model based on convolutional neural networks (CNN) is proposed, achieving accuracies of 97% and 88% on the AHCD dataset and the Hijja dataset, respectively, outperforming other models in the literature.
Abstract: Automatic handwriting recognition is an important component for many applications in various fields It is a challenging problem that has received a lot of attention in the past three decades Research has focused on the recognition of Latin languages’ handwriting Fewer studies have been done for the Arabic language In this paper, we present a new dataset of Arabic letters written exclusively by children aged 7–12 which we call Hijja Our dataset contains 47,434 characters written by 591 participants In addition, we propose an automatic handwriting recognition model based on convolutional neural networks (CNN) We train our model on Hijja, as well as the Arabic Handwritten Character Dataset (AHCD) dataset Results show that our model’s performance is promising, achieving accuracies of 97% and 88% on the AHCD dataset and the Hijja dataset, respectively, outperforming other models in the literature
93 citations
Cites background or methods from "Handwritten Arabic Numeral Recognit..."
...First, they introduced two changes to the MLP-based model [14]: the use of rectified linear unit (ReLU) as the activation of each neuron in input layer and hidden layer and the softmax function in the outer classifying layer....
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...[14] developed a set of 88 features representing samples of handwritten Arabic numerals....
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...This is better than the performance of models presented in [8] and [14], where they achieved accuracy values of 93....
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Posted Content•
TL;DR: A novel algorithm based on deep learning neural networks using appropriate activation function and regularization layer is proposed, which shows significantly improved accuracy compared to the existing Arabic numeral recognition methods.
Abstract: Handwritten character recognition is an active area of research with applications in numerous fields. Past and recent works in this field have concentrated on various languages. Arabic is one language where the scope of research is still widespread, with it being one of the most popular languages in the world and being syntactically different from other major languages. Das et al. \cite{DBLP:journals/corr/abs-1003-1891} has pioneered the research for handwritten digit recognition in Arabic. In this paper, we propose a novel algorithm based on deep learning neural networks using appropriate activation function and regularization layer, which shows significantly improved accuracy compared to the existing Arabic numeral recognition methods. The proposed model gives 97.4 percent accuracy, which is the recorded highest accuracy of the dataset used in the experiment. We also propose a modification of the method described in \cite{DBLP:journals/corr/abs-1003-1891}, where our method scores identical accuracy as that of \cite{DBLP:journals/corr/abs-1003-1891}, with the value of 93.8 percent.
82 citations
Cites background or methods from "Handwritten Arabic Numeral Recognit..."
...that has affected the method in [1], by applying dropout regularization....
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...[1] used a pre selected 88 features to feed into the MLP network, however we did not pre-select any features or reduce them....
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...[1] have devised a novel method for Arabic handwritten digit recognition with help of a multi layer perceptron (MLP) which can bring significant accuracy to the...
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...TABLE I PERFORMANCE COMPARISON OF PROPOSED METHODS AND METHOD DESCRIBED IN [1]...
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...We also propose a modification of the method described in [1], where our method scores identical accuracy as that of [1], with the value of 93....
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01 Jan 2017
TL;DR: In this article, the authors proposed a novel algorithm based on deep learning neural networks using appropriate activation function and regularization layer, which shows significantly improved accuracy compared to the existing Arabic numeral recognition methods.
Abstract: Handwritten character recognition is an active area of research with applications in numerous fields. Past and recent works in this field have concentrated on various languages. Arabic is one language where the scope of research is still widespread, with it being one of the most popular languages in the world and being syntactically different from other major languages. Das et al. [1] has pioneered the research for handwritten digit recognition in Arabic. In this paper, we propose a novel algorithm based on deep learning neural networks using appropriate activation function and regularization layer, which shows significantly improved accuracy compared to the existing Arabic numeral recognition methods. The proposed model gives 97.4 percent accuracy, which is the recorded highest accuracy of the dataset used in the experiment. We also propose a modification of the method described in [1], where our method scores identical accuracy as that of [1], with the value of 93.8 percent.
75 citations
TL;DR: This study proposes a new approach for Arabic handwritten digit recognition by use of restricted Boltzmann machine (RBM) and convolutional neural network (CNN) deep learning algorithms and shows that the proposed method significantly improves the accuracy rate.
Abstract: Handwritten digit recognition is an open problem in computer vision and pattern recognition, and solving this problem has elicited increasing interest. The main challenge of this problem is the design of an efficient method that can recognize the handwritten digits that are submitted by the user via digital devices. Numerous studies have been proposed in the past and in recent years to improve handwritten digit recognition in various languages. Research on handwritten digit recognition in Arabic is limited. At present, deep learning algorithms are extremely popular in computer vision and are used to solve and address important problems, such as image classification, natural language processing, and speech recognition, to provide computers with sensory capabilities that reach the ability of humans. In this study, we propose a new approach for Arabic handwritten digit recognition by use of restricted Boltzmann machine (RBM) and convolutional neural network (CNN) deep learning algorithms. In particular, we propose an Arabic handwritten digit recognition approach that works in two phases. First, we use the RBM, which is a deep learning technique that can extract highly useful features from raw data, and which has been utilized in several classification problems as a feature extraction technique in the feature extraction phase. Then, the extracted features are fed to an efficient CNN architecture with a deep supervised learning architecture for the training and testing process. In the experiment, we used the CMATERDB 3.3.1 Arabic handwritten digit dataset for training and testing the proposed method. Experimental results show that the proposed method significantly improves the accuracy rate, with accuracy reaching 98.59%. Finally, comparison of our results with those of other studies on the CMATERDB 3.3.1 Arabic handwritten digit dataset shows that our approach achieves the highest accuracy rate.
48 citations
TL;DR: The main objective of this paper is critically analyzing the current researches to identify the problem areas and challenges faced by the previous researchers and to provide many recommendations for future advances in the area.
Abstract: The paper is a comprehensive review of the current research trends in the area of Arabic language especially state-of-the-art approaches to highlight the current status of diverse research aspects of that area to facilitate the adaption and extension of previous systems into new applications and systems. The Arabic language has deep, widespread and unexplored scope to research although the tremendous effort and researches that had been done previously. Modern state-of-the-art methods and approaches with fewer errors are required according to the high speed of hardware and technology development. The focus of this article will be on the offline Arabic handwritten text recognition as it is one of the most important topics in the Arabic scope. The main objective of this paper is critically analyzing the current researches to identify the problem areas and challenges faced by the previous researchers. This identification is intended to provide many recommendations for future advances in the area. It also compares and contrasts technical challenges, methods and the performances of handwritten text recognition previous researches works. It summarizes the critical problems and enumerates issues that should be considered when addressing these tasks. It also shows some of the Arabic datasets that can be used as inputs and benchmarks for training, testing and comparisons. Finally, it provides a fundamental comparison and discussion of some of the remaining open problems and trends in that field.
29 citations
Cites result from "Handwritten Arabic Numeral Recognit..."
...The drawback of their paper was that they compared their work with a single paper [48] published in 2010, which may seem an old paper....
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References
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Book•
16 Jul 1998
TL;DR: Thorough, well-organized, and completely up to date, this book examines all the important aspects of this emerging technology, including the learning process, back-propagation learning, radial-basis function networks, self-organizing systems, modular networks, temporal processing and neurodynamics, and VLSI implementation of neural networks.
Abstract: From the Publisher:
This book represents the most comprehensive treatment available of neural networks from an engineering perspective. Thorough, well-organized, and completely up to date, it examines all the important aspects of this emerging technology, including the learning process, back-propagation learning, radial-basis function networks, self-organizing systems, modular networks, temporal processing and neurodynamics, and VLSI implementation of neural networks. Written in a concise and fluid manner, by a foremost engineering textbook author, to make the material more accessible, this book is ideal for professional engineers and graduate students entering this exciting field. Computer experiments, problems, worked examples, a bibliography, photographs, and illustrations reinforce key concepts.
29,130 citations
TL;DR: The nature of handwritten language, how it is transduced into electronic data, and the basic concepts behind written language recognition algorithms are described.
Abstract: Handwriting has continued to persist as a means of communication and recording information in day-to-day life even with the introduction of new technologies. Given its ubiquity in human transactions, machine recognition of handwriting has practical significance, as in reading handwritten notes in a PDA, in postal addresses on envelopes, in amounts in bank checks, in handwritten fields in forms, etc. This overview describes the nature of handwritten language, how it is transduced into electronic data, and the basic concepts behind written language recognition algorithms. Both the online case (which pertains to the availability of trajectory data during writing) and the off-line case (which pertains to scanned images) are considered. Algorithms for preprocessing, character and word recognition, and performance with practical systems are indicated. Other fields of application, like signature verification, writer authentification, handwriting learning tools are also considered.
2,653 citations
"Handwritten Arabic Numeral Recognit..." refers background in this paper
...The past work on OCR of handwritten alphabets and numerals has mostly found to concentrate on Roman script[3] related to English and some other European languages, and scripts related to Asian languages like Chinese [2], Korean, Japanese....
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TL;DR: In this article, the authors present the state of Arabic character recognition research throughout the last two decades and present the main objective of this paper is to present the current state of the research.
319 citations
22 Nov 2004
TL;DR: A moderately large database of Bangla handwritten character images is used for the recognition purpose and an MLP classifier is trained using a variant of the backpropagation algorithm that uses self-adaptive learning rates.
Abstract: A recognition scheme for handwritten basic Bangla (an Indian script) characters is proposed. No such work has been reported before on a reasonably large representative database. Here a moderately large database of Bangla handwritten character images is used for the recognition purpose. A handwritten character is composed of several strokes whose characteristics depend on the handwriting style. The strokes present in a character image are identified in a simple fashion and 10 certain features are extracted from each of them. These stroke features are concatenated in an appropriate order to form the feature vector of a character image on the basis of which an MLP classifier is trained using a variant of the backpropagation algorithm that uses self-adaptive learning rates. The training and test sets consist respectively of 350 and 90 sample images for each of 50 Bangla basic characters. A separate validation set is used for termination of training of the MLP.
76 citations
TL;DR: A handwritten Chinese character off-line recognizer based on contextual vector quantization (CVQ) of every pixel of an unknown character image has been constructed and the CVQ-based language model is the most effective one upgrading the recognition rate by 10.4 percent on the average.
Abstract: A handwritten Chinese character off-line recognizer based on contextual vector quantization (CVQ) of every pixel of an unknown character image has been constructed. Each template character is represented by a codebook. When an unknown image is matched against a template character, each pixel of the image is quantized according to the associated codebook by considering not just the feature vector observed at each pixel, but those observed at its neighbors and their quantization as well. Structural information such as stroke counts observed at each pixel are captured to form a cellular feature vector. Supporting a vocabulary of 4616 simplified Chinese characters and alphanumeric and punctuation symbols, the writer-independent recognizer has an average recognition rate of 77.2 percent. Three statistical language models for postprocessing have been studied for their effectiveness in upgrading the recognition rate of the system. Among them, the CVQ-based language model is the most effective one upgrading the recognition rate by 10.4 percent on the average.
51 citations
"Handwritten Arabic Numeral Recognit..." refers background in this paper
...The past work on OCR of handwritten alphabets and numerals has mostly found to concentrate on Roman script[3] related to English and some other European languages, and scripts related to Asian languages like Chinese [2], Korean, Japanese....
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