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This paper presents a script invariant handwritten digit recognition system for identifying digits written in five popular scripts of Indian subcontinent, namely, Indo-Arabic, Bangla, Devanagari, Roman, and Telugu. A lot of work has been done for various non-Indic scripts particularly, in case of Roman, but, in case of Indic scripts, the research is limited. As the handwritten digits are not of the same size, thickness, style, and orientation, therefore, these challenges are to be faced to resolve this problem. CNN is a special type of multy-layer neural network, being trained with an optimized version of the back-propagation learning algorithm.CNN is designed to recognize visual patterns directly from pixel images with minimal preprocessing, being capable to recognize patterns with extreme variability (such as handwritten characters, and with robustness to distortions and simple geometric transformations.The main contributions of this paper are related to theoriginal methods for increasing the efï¬ciency of the learning algorithm by preprocessing the images before the learning process and a method for increasing the precision and performance for real-time applications, by removing the non useful information from the background.By combining these strategies we have obtained an accuracy of 96.76%, using as training set the NIST (National Institute of Standards and Technology database.Ī Study of Moment Based Features on Handwritten Digit Recognitionįull Text Available Handwritten digit recognition plays a significant role in many user authentication applications in the modern world. A theoretical framework for the neural networks used to classify the handwritten digits is also presented.The classiï¬cation task is performed using a Convolutional Neural Network (CNN. Handwritten Digits Recognition Using Neural Computingĭirectory of Open Access Journals (Sweden)įull Text Available In this paper we present a method for the recognition of handwritten digits and a practical implementation of this method for real-time recognition. Finally, experimental results from the well-known MNIST dataset show that the proposed Q-ADBN has a superiority to other similar methods in terms of accuracy and running time. Second, Q-ADBN receives Q-function (reward signal) during recognition of the current states, and the final handwritten digits recognition is implemented by maximizing the Q-function using Q-learning algorithm. First, Q-ADBN extracts the features of original images using an adaptive deep auto-encoder (ADAE), and the extracted features are considered as the current states of Q-learning algorithm. The adaptive deep Q-learning strategy combines the feature-extracting capability of deep learning and the decision-making of reinforcement learning to form an adaptive Q-learning deep belief network (Q-ADBN). In this paper, an adaptive deep Q-learning strategy is proposed to improve accuracy and shorten running time for handwritten digit recognition.
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Although many deep learning-based classification algorithms are studied for handwritten digits recognition, the recognition accuracy and running time still need to be further improved. Handwritten digits recognition is a challenging problem in recent years. Qiao, Junfei Wang, Gongming Li, Wenjing Chen, Min
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(author)Īn adaptive deep Q-learning strategy for handwritten digit recognition. A database consisting of 4000 samples of multi- digits consisting only two digits from 10-50 and other matching numerals have been collected by 50 users and the experimental results of proposed method show that an accuracy of 86.89% is achieved. Only one sample is required to train the network for each pair of multi- digit numerals. Therefore, SOM (Self-Organizing Map), a NN (Neural Network) method is used which can recognize digits with various writing styles and different font sizes. The recognition of Sindhi digits is a difficult task due to the various writing styles and different font sizes. However, the literature reviewed does not show any remarkable work done for Sindhi numerals recognition.
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A remarkable work has been done for recognition of isolated handwritten characters as well as digits in many languages like English, Arabic, Devanagari, Chinese, Urdu and Pashto. Handwritten digits recognition is one of the challenging tasks and a lot of research is being carried out since many years. In this research paper a multi- digit Sindhi handwritten numerals recognition system using SOM Neural Network is presented. International Nuclear Information System (INIS) Multi- digit handwritten sindhi numerals recognition using som neural network
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