handwritten  character
196173607
Handwritten Character Recognition by using Convolutional Deep Neural Network; Review
Handwritten character recognition is an important domain of research with implementation in varied fields. Past and recent works in this field focus on diverse languages to utilize the character recognition in automated data entry applications. Deep Neural network studies recognize the individual characters in the form images. The reliance of each recognition, which is provided by the neural network as part of the ranking result, is one of the things used to customize the implementation to the request of the client. Convolutional Deep neural network model is reviewed to recognize the handwritten characters in this study. This model, initially, learned a useful set of support by using core and local receptive areas and then a densely connected network layers are employed for the discernment task.
Baki Koyuncu Hakan Koyuncu
198949326
Approaches for Offline Cursive Handwritten Character Recognition
Handwritten Character Recognition (HCR) plays an important role in the retrieval of information from pixel based images to searchable text formats. For instance (HCR) nonlinear normalization of character size, and feature compression of higher dimensional original features are studied as pre processing and feature extraction techniques for a statistical character classifier to improve the recognition accuracy of handwritten character recognition. A high speed pre classification technique using a linear discriminant function is employed to improve the recognition speed. The nonlinear normalization is also utilized to supply the lack of the training samples by artificially generating character samples. The Inception V3 network is trained with character images consisting of noises which are collected from receipts and newspapers. Analysis and discussion were also made on how the different layer's properties of neural network affects the HCR's performance and training time. The proposed deep learning based HCR has shown better accuracy than conventional methods of HCR and has the potential to overcome in the text.
Varsha Vishwakarma Hylish James
198324192
Conditional DCGAN's Challenge: Generating Handwritten Character Digit, Alphabet and Katakana
Developing deep learning models has a great potential in assisting human tasks involving design and creativity. This study deals with generating handwritten characters using deep learning techniques. The task is not simply generating images randomly, but generating them conditionally, making a distinction according to the UI designates. To solve this task, we constructed the Conditional DCGAN model which includes the techniques from DCGAN and Conditional GAN. We tried training the models to be able to generate conditional images by adding label information as input to the Generator. Deep learning experiments were performed using 141319 training data consisting of 96 kinds of characters including digits, Roman alphabets and Katakana. The Generator trained by inputting random noise concatenated with the 96 kinds of characters, could generate each kind of character by just adding the appropriate label information.
Rina Komatsu
54810325
ARABIC HANDWRITTEN CHARACTER RECOGNITION BASED ON DEEP CONVOLUTIONAL NEURAL NETWORKS
The automatic analysis and recognition of offline Arabic handwritten characters from images is an important problem in many applications. Even with the great progress of recent research in optical character recognition, a few problems still wait to be solved, especially for Arabic characters. The emergence of Deep Neural Networks promises a strong solution to some of these problems. We present a deep neural network for the handwritten Arabic character recognition problem that uses convolutional neural network (CNN) models with regularization parameters such as batch normalization to prevent overfitting. We applied the Deep CNN for the AIA9k and the AHCD databases and the classification accuracies for the two datasets were 94.8% and 97.6% respectively. A study of the network performance on the EMNIST and a form based AHCD dataset were performed to aid in the analysis.
Khaled S Younis
232770593
Convolutional ensembles for Arabic Handwritten Character and Digit Recognition
A learning algorithm is proposed for the task of Arabic Handwritten Character and Digit recognition. The architecture consists on an ensemble of different Convolutional Neural Networks. The proposed training algorithm uses a combination of adaptive gradient descent on the first epochs and regular stochastic gradient descent in the last epochs, to facilitate convergence. Different validation strategies are tested, namely Monte Carlo Cross Validation and K fold Cross Validation. Hyper parameter tuning was done by using the MADbase digits dataset. State of the art validation and testing classification accuracies were achieved, with average values of 99.74% and 99.47% respectively. The same algorithm was then trained and tested with the AHCD character dataset, also yielding state of the art validation and testing classification accuracies: 98.60% and 98.42% respectively.
PeerJ. Computer science
Iam Palatnik de Sousa
56599708
An Efficient Handwritten Character Recognition Using Quantum Multilayer Neural Network (QMLNN) Architecture: Quantum Multilayer Neural Network
This chapter proposes a quantum multi layer neural network (QMLNN) architecture suitable for handwritten character recognition in real time, assisted by quantum backpropagation of errors calculated from the quantum inspired fuzziness measure of network output states. It is composed of three second order neighborhood topologybased inter connected layers of neurons represented by qubits known as input, hidden, and output layers. The QMLNN architecture is a feed forward network with standard quantum backpropagation algorithm for the adjustment of its weighted interconnection. QMLNN self organizes the quantum fuzzy input image information by means of the quantum backpropagating errors at the intermediate and output layers of the architecture. The interconnection weights are described using rotation gates. After the network is stabilized, a quantum observation at the output layer destroys the superposition of quantum states in order to obtain true binary outputs. An Efficient Handwritten Character Recognition Using Quantum Multilayer Neural Network (QMLNN) Architecture: Quantum Multilayer Neural Network
Debanjan Konar Suman Kalyan Kar
52294394
HCRCaaS: A Handwritten Character Recognition Container as a Service Based on QoS Guarantee Algorithm
Handwritten character recognition (HCR) is a mainstream mobile device input method that has attracted significant research interest. Although previous studies have delivered reasonable recognition accuracy, it remains difficult to directly embed the advanced HCR service into mobile device software and obtain excellent but fast results. Cloud computing is a relatively new online computational resource provider which can satisfy the elastic resource requirements of the advanced HCR service with high recognition accuracy. However, owing to the delay sensitivity of the character recognition service, the performance loss in the traditional cloud virtualization technology (e.g. kernel based virtual machine (KVM) may impair the performance. In addition, the improper computational resource scheduling in cloud computing impairs not only the performance but also the resource utilization. Thus, the HCR online service is required to guarantee the performance and improve the resource utilization of the HCR service in cloud computing. To address these problems, in this paper, we propose an HCR container as a service (HCRCaaS) in cloud computing. We address several key contributions: (1) designing an HCR engine on the basis of deep convolution neutral networks as a demo for an advanced HCR engine with better recognition accuracy, (2) providing an isolated lightweight runtime environment for high performance and easy expansion, and (3) designing a greedy resource scheduling algorithm based on the performance evaluation to optimize the resource utilization under a quality of service (QoS) guaranteeing. Experimental results show that our system not only reduces the performance loss compared with traditional cloud computing under the advanced HCR algorithm but also improves the resource utilization appropriately under the QoS guaranteeing. This study also provides a valuable reference for other related studies.
Sci. Program.
Lei Li Xue Gao Lianwen Jin
53483855
Deep Learning based English Handwritten Character Recognition
Abstract: In this paper we propose a convolutional neural network based handwritten character recognitionusing SVM as an end classifier. The learning model is based on CNN (Convolutional Neural Network) which is used as a feature extraction tool. The proposed method is more efficient than other existing methods and will be more efficient than modifying the CNN with complex architecture. The recognition rate achieved by the proposed method is 93.3% which is greater than other existing methods. The computation time of training phase is 13.14sec and that of testing phase is 13.27 sec. The proposed system was validated by 6 validation points. The overall accuracy of system is 93%
Somil Gupta Tanvi Bansal Manish Kumar Sharma
203646609
English Handwritten Character Recognition using Convolutional Neural Network CNN
Character recognition is one of the most important research fields of image processing and pattern recognition. Character recognition is generally known as Optical Character Recognition (OCR).OCR is the process of electronic translation of handwritten images or typewritten text into machine editable text. It becomes very difficult if there are lots of paper based information on companies and offices. Because they want to manage a huge volume of documents and records. Computers can work much faster and more efficiently than human. It is used to perform many of the tasks required for efficient document and content management. But computer knows only alphanumeric characters as ASCII code. So computer cannot distinguish character or a word from a scanned image. In order to use the computer for document management, it is required to retrieve alphanumeric information from a scanned image. There are so many methods which are currently used for OCR and are based on different languages. The existing method like Artificial Neural Network (ANN) based on English Handwritten character recognition needs the features to be extracted and also the performance level is low. So a Convolutional Neural Network (CNN) based English handwritten character recognition method is used. It's a deep machine learning method for which it doesn't want to extract the features and also a fast method for character recognition.
M Safna
67911454
Malayalam Handwritten Character Recognition Using AlexNet Based Architecture
This research article proposes a new handwritten Malayalam character recognition model based on AlexNet based architecture. The Malayalam language consists of a variety of characters having similar features, thus, differentiating characters is a challenging task. A lot of handcrafted feature extraction methods have been used for the classification of Malayalam characters. Convolutional Neural Networks (CNN) is one of the popular methods used in image and language recognition. AlexNet based CNN is proposed for feature extraction of basic and compound Malayalam characters. Furthermore, Support Vector Machine (SVM) is used for classification of the Malayalam characters. The 44 primary and 36 compound Malayalam characters are recognised with better accuracy and achieved minimal time consumption using this model. A dataset consisting of about 180,000 characters is used for training and testing purposes. This proposed model produces an efficiency of 98% with the dataset. Further, a dataset for Malayalam characters is developed in this research work and shared on Internet
Ajay James Jyoti Manjusha C Saravanan