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handwritten digit recognition based on ghost imaging with deep learning

Chinese Physics B 2021年第5期30卷 367-372页

作者： Xing He Sheng-Mei Zhao Le Wang Institute of Signal Processing and Transmission Nanjing University of Posts and TelecommunicationsNanjing 210003China Key Laboratory of Broadband Wireless Communication and Sensor Network Technology(Ministry of Education) Nanjing 210003China

We present a ghost handwritten digit recognition method for the unknown handwritten digits based on ghost imaging(GI)with deep neural network,where a few detection signals from the bucket detector,generated by the cosine transform speckle,are used as the characteristic information and the input of the designed deep neural network(DNN),and the output of the DNN is the *** results show that the proposed scheme has a higher recognition accuracy(as high as 98%for the simulations,and 91%for the experiments)with a smaller sampling ratio(say 12.76%).With the increase of the sampling ratio,the recognition accuracy is *** with the traditional recognition scheme using the same DNN structure,the proposed scheme has slightly better performance with a lower complexity and non-locality *** proposed scheme provides a promising way for remote sensing.

关键词： ghost imaging handwritten digit recognition ghost handwritten recognition deep learning

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handwritten digit recognition: benchmarking of state-of-the-art techniques

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PATTERN recognition 2003年第10期36卷 2271-2285页

作者： Liu, CL Nakashima, K Sako, H Fujisawa, H Hitachi Ltd Cent Res Lab Kokubunji Tokyo 1858601 Japan

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 chaincode feature, gradient feature, profile structure feature, and peripheral direction contributivity. 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 accuracies. Relatively, the chaincode feature and the gradient feature show advantage over other features, and the profile structure feature shows efficiency as a complementary feature. The SVC with RBF kernel (SVC-rbf) gives the highest accuracy in most cases but is extremely expensive in storage and computation. Among the non-SV classifiers, the polynomial classifier and DLQDF give the highest accuracies. The results of non-SV classifiers are competitive to the best ones previously reported on the same databases. (C) 2003 Pattern recognition Society. Published by Elsevier Ltd. All rights reserved.

关键词： handwritten digit recognition the state of the art feature extraction pattern classification discriminative learning support vector classifier

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handwritten digit recognition: investigation of normalization and feature extraction techniques

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PATTERN recognition 2004年第2期37卷 265-279页

作者： Liu, CL Nakashima, K Sako, H Fujisawa, H Hitachi Ltd Cent Res Lab Kokubunji Tokyo 1858601 Japan

The performance evaluation of various techniques is important to select the correct options in developing character recognition systems. In our previous works, we have proposed aspect ratio adaptive normalization (ARAN) and have evaluated the performance of state-of-the-art feature extraction and classification techniques. For this time, we will propose some improved normalization functions and direction feature extraction strategies and will compare their performance with existing techniques. We compare ten normalization functions (seven based on dimensions and three based on moments) and eight feature vectors on three distinct data sources. The normalization functions and feature vectors are combined to produce eighty classification accuracies to each dataset. The comparison of normalization functions shows that moment-based functions outperform the dimension-based ones and the aspect ratio mapping is influential. The comparison of feature vectors shows that the improved feature extraction strategies outperform their baseline counterparts. The gradient feature from gray-scale image mostly yields the best performance and the improved NCFE (normalization-cooperated feature extraction) features also perform well. The combined effects of normalization, feature extraction, and classification have yielded very high accuracies on well-known datasets. (C) 2003 Pattern recognition Society. Published by Elsevier Ltd. All rights reserved.

关键词： handwritten digit recognition normalization aspect ratio mapping direction feature NCFE gradient feature

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handwritten digit recognition by means of a holographic associative memory

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EXPERT SYSTEMS WITH APPLICATIONS 1998年第3-4期15卷 399-403页

作者： Mayora-Ibarra, O Curatelli, F Univ Genoa DIBE I-16145 Genoa Italy

In this paper, a holographic associative memory (HAM) is proposed for recognizing handwritten variations of the ten digits. First, the handwritten characters were taken from the NIST standard database in order to extract relevant features from each one of them. Each digit was thus represented as a vector of 112 features constructed by dividing each character into 16 equal-sized partitions, each one used to extract seven different features for recognition. Second, these feature vectors, and reduced combinations of them, were input to train several HAM systems respectively. Then, all these memories were tested with a new set of patterns and the lowest-error HAM was chosen as the best training set. The features used in this last memory were taken as the most significant variables for describing each digit in the database. Finally, these most significant features were used to show the behaviour of the recognition rate when training the HAM with reduced training sets. Some final conclusions are reported and future work directions are proposed. (C) 1998 Elsevier Science Ltd. All rights reserved.

关键词： pattern recognition handwritten digit recognition holographic associative memory

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handwritten digit recognition by a mixture of local principal component analysis

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NEURAL PROCESSING LETTERS 1998年第3期8卷 241-251页

作者： Zhang, BL Fu, MY Yan, H Univ Sydney Dept Elect Engn Sydney NSW 2006 Australia Univ Newcastle Dept Elect & Comp Engn Newcastle NSW 2308 Australia

Mixture of local principal component analysis (PCA) has attracted attention due to a number of benefits over global PCA. The performance of a mixture model usually depends on the data partition and local linear fitting. In this paper, we propose a mixture model which has the properties of optimal data partition and robust local fitting. Data partition is realized by a soft competition algorithm called neural 'gas' and robust local linear fitting is approached by a nonlinear extension of PCA learning algorithm. Based on this mixture model, we describe a modular classification scheme for handwritten digit recognition, in which each module or network models the manifold of one of ten digit classes. Experiments demonstrate a very high recognition rate.

关键词： neural networks mixture of principal component analysis handwritten digit recognition

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handwritten digit recognition based on Improved VGG16 Network 10

Handwritten Digit Recognition based on Improved VGG16 Networ...

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10th International Conference on Graphics and Image Processing (ICGIP)

作者： Cheng Shuhong Shang Guochao Zhang Li Yanshan Univ Coll Elect Engn Qinhuangdao 066004 Hebei Peoples R China Qinhuangdao Tech Coll Coll Elect Engn Qinhuangdao 066001 Hebei Peoples R China

ISBN: (数字)9781510628298

ISBN: (纸本)9781510628298

This paper presents a new strategy of handwritten digit recognition based on the improved VGG16 model that aims at the lack of texture features, the large differences between handwritings, and the difficulty of extracting effective information. In order to maximize the efficiency of the improved VGG16 model, an automatic drop-based learning rate scheduling is proposed to improve the SGD algorithm in the learning procedure. And automatic adaptation technique of the SGD optimizer's learning rate parameter according to the changes of accuracy rate in the previous training iterations is utilized for the training of the model, which not only apparently improves the learning results, but also speeds up the training convergence. The improved VGG16 model was evaluated on the extended MNIST dataset, achieving a high recognition accuracy rate of 99.97%. Experimental results demonstrate that the improved VGG16 model has obviously higher recognition accuracy than traditional classifiers, has stronger feature extraction ability and can meet the requirements of handwritten digits classification and recognition.

关键词： Improved VGG16 model learning rate annealing algorithm SGD optimizer handwritten digit recognition

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handwritten digit recognition by Multi-Objective Optimization of Zoning Methods

Handwritten Digit Recognition by Multi-Objective Optimizatio...

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13th International Conference on Frontiers in Handwriting recognition (ICFHR)

作者： Impedovo, S. Pirlo, G. Mangini, F. M. Univ Bari A Moro Dipartimento Informat I-70126 Bari Italy

ISBN: (纸本)9780769547749;9781467322621

This paper addresses the use of multi-objective optimization techniques for optimal zoning design in the context of handwritten digit recognition. More precisely, the Non-dominant Sorting Genetic Algorithm II (NSGA II) has been considered for the optimization of Voronoi-based zoning methods. In this case both the number of zones and the zone position and shape are optimized in a unique genetic procedure. The experimental results point out the usefulness of multi-objective genetic algorithms for achieving effective zoning topologies for handwritten digit recognition.

关键词： handwritten digit recognition Genetic Algorithms NSGA II Zoning Topology Voronoi Diagrams

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handwritten digit recognition Using Bayesian ResNet

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SN Computer Science 2021年第5期2卷 399页

作者： Mhasakar, Purva Trivedi, Prapti Mandal, Srimanta Mitra, Suman K. Dhirubhai Ambani Institute of Information and Communication Technology Gujarat Gandhinagar India

The problem of handwritten digit recognition has seen various developments in the recent times, especially in neural network domain. The methods based on neural network work quite effectively for the seen classes of data by providing deterministic results. However, these methods tend to behave in similar fashion even for unseen class of data. For example, a neural network trained on English language digits will give a deterministic prediction even when tested on digits of other languages. Hence, it is required to predict uncertainty for such methods in this scenario. In this paper, we employ Bayesian inference into the existing ResNet18 framework to bring out uncertainty for handwritten digit recognition when there is a new class of test digit. We term the new architecture as B-ResNet. The novel B-ResNet is first of its kind to be investigated for the handwritten digit recognition. Various experiments on datasets of English, Devanagari, Gujarati, Bengali digits and their all possible combinations demonstrate the efficiency and performance of the B-ResNet for hand written digit recognition. © 2021, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Bayesian approach Deep learning handwritten digit recognition ResNet

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handwritten digit recognition using DAISY Descriptor: A Study 5

Handwritten Digit Recognition using DAISY Descriptor: A Stud...

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5th International Conference on Emerging Applications of Information Technology (EAIT)

作者： Chatterjee, Agneet Malakar, Samir Sarkar, Ram Nasipuri, Mita Jadavpur Univ Dept Comp Sci & Engn Kolkata India Asutosh Coll Dept Comp Sci Kolkata India

ISBN: (纸本)9781538637197

handwritten digit recognition is a highly evolved research domain. The major issues that make this domain challenging are different photometric discrepancies, along with computation complexity. A script invariant feature vector is designed here based on the concept of the DAISY descriptor which has previously been applied in different research domains. We have applied this feature descriptor after suitable customization to fit it into the aforesaid classification problem. We have tested the same on handwritten digits written in four different scripts namely Arabic, Bangla, Devanagari and Roman. Bangla dataset is in-house, while the remaining are the standard databases. Experimental results demonstrate the effectiveness of the said feature descriptor for digit recognition. It is a computationally inexpensive approach when compared to other state-of-the-art prevalent architectures like LSTM or CNN.

关键词： handwritten digit recognition DAISY descriptor Arabic Bangla Devanagari Roman

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handwritten digit recognition via Active Belief Decision Trees 35

Handwritten Digit Recognition via Active Belief Decision Tre...

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第35届中国控制会议

作者： LI Ziyi MA Liyao KE Xiaolu WANG Yong Department of Automation University of Science and Technology of China

ISBN: (纸本)9781509009107

Considering uncertainty within the learning methods is attracting more and more attentions in recent years,yet not much work has been done when it comes to the area of handwritten digit *** this paper,the active belief decision tree is used to handle the epistemic uncertainty in the practical *** detailed feature extraction method for belief decision trees is proposed and *** the conditions such as lack of knowledge about true labels,few instances to train the model and low-dimensional image features,we still achieve a good classification performance as shown in experiments with data from the commonly used MNIST handwritten digits benchmark.

关键词： handwritten digit recognition epistemic uncertainty ABC45 algorithm evidential likelihood belief functions

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