ABSTRACT success and challenging machine learning applications such as

ABSTRACT Machine learning enables computers to get into a mode of self-learning without being explicitly programmed. When exposed to new data, computer programs, are enabled to learn, grow, change, and develop by themselves. Machine learning is one of the most recent exciting technologies, recently handwritten digit recognition becomes vital scope and it is appealing many researchers because of its using in variety of machine learning and computer vision applications. However, there are deficient works accomplished on pattern digits because digits are more challenging than English patterns. Hence, the lacking research of using digits endeavors us to dig deeper by creating our challenge Handwritten Digits. As a challenging dataset is used for evaluation, a robust deep convolutional neural network is used for classification and superior results are achieved.Keywords— In this article, we review the handwritten digit recognition by outlining the following: the aim of handwritten digit recognition, Neural Network, ANN, Digit Extraction algorithm, Image acquisition, Digital recognition.INTRODUCTIONDeep Convolutional Neural Networks (CNNs) becomes one of the most appealing approaches and has been a crucial factor in variety of recent success and challenging machine learning applications such as challenge ImageNet, object detection , image segmentation, and face recognition . Therefore, CNNs is considered our main model for our challenging tasks of image classification. Specifically, it is used for handwriting digits recognition which is one of high academic and business transactions. Handwriting digit recognition application is used in different tasks of our real-life time purposes. Precisely, it is used in banks for reading checks, post offices for sorting letter, and many other related tasks. Apparently English Handwriting datasets are widely available and significant achievements have been made for English digit datasets such as CENPARMI , CEDAR, and MNIST, However, there are rare works accomplished on Arabic digit datasets for many reasons. One of critical factor that can influence working on Arabic dataset is lacking to dataset. The unavailability of dataset can be one of the essential factors that candiminish working on Arabic datasets. Hence, deficiency of large challenging Digit dataset strives us to extensively working on creating a largest and most challenging dataset which contains more than 45,000 patterns. Furthermore, we investigate and demonstrate a powerful DCNN used for classification. Not only designing powerful DCNN is presented but also critical parameters of CNN is carefully selected and tuned to produce final concrete model which achieves superior results.LITERATURE SURVEY1. Yann LeCun, L. D. Jackel, Leon Bottou , Corinna Cortes,” A comparison on handwritten digit recognition”This paper compares the performance of several classifier algorithms on a standard database of handwritten digits. We consider not only raw accuracy, but also training time, recognition time, and memory requirements. When available, we report measurements of the fraction of patterns that must be rejected so that the remaining patterns have misclassification rates less than a given threshold.2. D. C. Ciresan, U. Meier, L. M. Gambardella, and J. Schmidhuber, “Deep, big, simple neural nets for handwritten digit recognition”In this paper, the author reviews the disadvantage of how using online back propagation for plain multilayer perceptron yields a very low error rate of 0.35% on the MNIST handwritten digits benchmark. In order to get a better result they suggest the use of a neural network with many hidden layers having many neurons per layer along with numerous deformed training images to avoid overfitting, and graphics cards to greatly speed up learning.3. Y. Le cun, B. Boser, J. S. Denker, D. Henderson, R. E. Howard, W. Hubbard, and L. D. Jackel,”Handwritten recognition with a Back-propogation networks”It presents an application of back-propagation networks to handwritten digit recognition. Minimal preprocessing of the data was required, but architecture of the network was highly constrained and specifically designed for the task. The input of the network consists of normalized images of isolated digits. The method has 1% error rate and about a 9% reject rate on zipcode digits provided by the U.S. Postal Service.4.”Cheng-LinLiu,Kazuki,Hiroshi,Kazuki,” Handwritten digit recognition: benchmarking of state-of-the-art techniques”This paper presents the results of handwritten digit recognition on well-known image databases using state-of-the-art feature extraction and classification techniques. The tested databases are CENPARMI, CEDAR, and MNIST. On the test data set of each database, 80 recognition accuracies are given by combining eight classifiers with ten feature vectors. The features include chain code feature, gradient feature, profile structure feature, and peripheral direction contributively. The gradient feature is extracted from either binary image or gray-scale image. The classifiers include the k-nearest neighbor classifier, three neural classifiers, a learning vector quantization classifier, a discriminative learning quadratic discriminant function (DLQDF) classifier, and two support vector classifiers (SVCs). All the classifiers and feature vectors give high recognition accuracies5.”A trainable feature extractor for handwritten digit recognition”, Fabien Lauer, Ching Y. Suen, Gerard BlochThis focuses on the problems of feature extraction and the recognition of handwritten digits. A trainable feature extractor based on the LeNet5 convolutional neural network architecture is introduced to generated by affine transformations and elastic distortions. Experiments are performed on the well-known MNIST database to validate the method and the results show that the system can outperform both SVMs and LeNet5 while providing performances comparable to the best performance on this database. Moreover, an analysis of the errors is conducted to discuss possible means of enhancement and their limitations.6.J.S Denker, B.Boser, “Backpropagation Applied to Handwritten Zip Code Recognition”The ability of learning networks to generalize can be greatly enhanced by providing constraints from the task domain. This paper demonstrates how such constraints can be integrated into a backpropagation network through the architecture of the network. This approach has been successfully applied to the recognition of handwritten zip code digits provided by the U.S. Postal Service. A single network learns the entire recognition operation, going from the normalized image of the character to the final classification.7. Xiaoxiao Niu, Ching Y. Suen, “A novel hybrid CNN–SVM classifier for recognizing handwritten digits”This paper presents a hybrid model of integrating the synergy of two superior classifiers: Convolutional Neural Network (CNN) and Support Vector Machine (SVM), which have proven results in recognizing different types of patterns. solve the first problem in a black box scheme without prior knowledge on the data. The classification task is performed by support vector machines to enhance the generalization ability of LeNet5. In order to increase the recognition rate, new training samples areIn this model, CNN works as a trainable feature extractor and SVM performs as a recognizer. This hybrid model automatically extracts features from the raw images and generates the predictions. Experiments have been conducted on the well-known MNIST digit database. Comparisons with other studies on the same database indicate that this fusion has achieved better results: a recognition rate of 99.81% without rejection, and a recognition rate of 94.40% with 5.60% rejection. These performances have been analyzed with reference to those by human subjects.IMPLEMENTATIONA noiseless image is segmented if it contains more than one digit. The previous segments are normalized to a common size to be suits as an input for the neural network. As a last step before the recognition process, thinning and skeletonaization process are applied on each segment. Finally, digits within the image are recognized using Neural Networks. Our system is designed to deal with any field where the digits could be used for example in bank checks, postal arrangement according to zip-code, car plates and any application forms that deals with numbers.We conclude that our Neural Network approach to recognizing Handwritten digits proved as a viable concept. Further refinement of the networks will certainly produce higher recognition accuracy while increasing the robustness of the solution. Scientists and researchers are still interested in this area, because it has many challenges until now ,and the system involves a verity of process operate sequentially to achieve the goal, many issues related to those process have not been touched, as the needed in the future those issues can be studied and tested under the proposed system and some of the following can be consider as the base for the future work, like check robustness in noisy setting and with different random initializations and skew detection and correction to the digits.RESULTThe algorithm is the Error Back propagation learningalgorithm for a layered feed forward network and thisalgorithm has many successful applications for trainingmultilayer neural networks. In this paper the last waveformshows that the weight value becomes constant to minimizethe error at the output. Sothe output remains constant whichis the objective of this paper.CONCLUSIONAfter reading through the papers, it is clearly understood that using neural network approach to recognize handwritten digit is easier approach as it extracts features on its own and is able to train with good enough accuracy.We can conclude that we reached the computer to the human’s brain by the importance use of isolated digits recognition for different applications. This recognition starts with acquiring the image to be preprocessed throw a number of steps. As an important point, classification and recognition have to be done to gain a numeral text. In a final conclusion, neural network seems to be better than other techniques used for recognition.ACKNOWLEDGMENTThis paper was written under the guidance of Prof. Ganeshayya Shidaganti, Department of Computer Science Engineering, Ramaiah Institute of Technology. We thank him for his support and useful inputs.REFERENCES1. “Handwritten Digit Recognition: Bench marking of state of the art techniques”, Cheng-Lin Liu, Kazuki Nakashima, Hiroshi Sako, Hiromichi Fujisawa.2. “A trainable feature extractor for handwritten digit recognition”, Fabien Lauer, Ching Y Suen, Gerard Bloch.3. “Backpropagation Applied to Handwritten Zip Code Recognition”, Y. LeCun, B. Boser, J. S. Denker, D. Henderson4. “Reading Digits in Natural Images with Unsupervised Feature Learning”, Yuval Netzer1 , Tao Wang , Adam Coates , Alessandro Bissacco , Bo Wu , Andrew Y. Ng5. “A novel hybrid CNN–SVM classifier for recognizing handwritten digits”, Xiao-Xiao Niu, Ching Y.Suen6. “Representation and recognition of handwritten digits using deformable templates”, A.K. Jain, D. Zongker7. “An overview of character recognition focused on off-line handwriting” , N. Arica , F.T. Yarman-Vural8. Handwritten Digit Recognition Using Convolutional Neural Networks”, Haider A. Alwzwazy, Hayder M. Albehadili, Younes S. Alwan, Naz E. Islam