Recognition of printed Chinese characters by using Neural Network

Khawaja, Attaullah; Shen, Tingzhi; Memon, Noor Mohammad; Rajpar, Altaf Hussain

doi:10.1109/inmic.2006.358156

Cited by 14 publications

(7 citation statements)

References 3 publications

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“…Hence, algorithms are necessary key factors for the identification and recognition of each character [52]. The neuron model which is used in ANNs is illustrated in Figure 7 [53].…”

Section: Post-processingmentioning

confidence: 99%

Scene Text Recognition using Artificial Neural Network: A Survey

Kumar¹,

Kumar²,

Mishra³

2016

IJCA

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Nowadays, scene text recognition has become an important emerging area of research in the field of image processing. In image processing, character recognition boosts the complexity in the area of Artificial Intelligence. Character recognition is not easy for computer programs in comparison to humans. In the broad spectrum of things, it may consider that recognizing patterns is the only thing which humans can do well and computers cannot. There are many reasons including various sources of variability, hypothesis and absence of hard-and-fast rules that define the appearance of a visual character. Hence; there is an unavoidable requirement for heuristic deduction of rules from different samples. This review highlights the superiority of artificial neural networks, a popular area of Artificial Intelligence, over various other available methods like fuzzy logic and genetic algorithm. In this paper, two methods are listed for character recognition -offline and online. The -Offline‖ methods include Feature Extraction, Clustering, and Pattern Matching. Artificial neural networks use the static image properties. The online methods are divided into two methods, k-NN classifier and direction based algorithm. Thus, the scale of techniques available for scene text recognition deserves an admiration. This review gives a detail survey of use of artificial neural network in scene text recognition.

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“…Hence, algorithms are necessary key factors for the identification and recognition of each character [52]. The neuron model which is used in ANNs is illustrated in Figure 7 [53].…”

Section: Post-processingmentioning

confidence: 99%

Scene Text Recognition using Artificial Neural Network: A Survey

Kumar¹,

Kumar²,

Mishra³

2016

IJCA

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“…Authors in [24] use 8 x 8 symmetrical zones where the percentage of black pixels in each zone is calculated while in [25], the total number of black pixels is calculated separately for each line in the horizontal and vertical direction for each zone. Authors in [26] and [27] develop a non-symmetrical zoning, with very good results.…”

Section: Zoningmentioning

confidence: 99%

Document zone content classification for technical document images using Artificial Neural Networks and Support Vector Machines

İbrahim¹,

Isa

Rajkumar

et al. 2009

2009 Second International Conference on the Applications of Digital Information and Web Technologies

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Artificial Neural Networks (ANN) are a classic pattern classifier and widely applicable to various problems and are relatively easy to use. Three of the most popular ANNs are Multilayer Perceptron (MLP) with Backpropagation learning algorithm, Self Organizing Map (SOM) and Recurrent Neural Network (RNN). Support Vector Machines (SVM) have gained great interest in the last few years in pattern recognition. Thus, this research compares the recognition performance of text and non-text images (text, table, figure and graph) from technical document images based on the pixel intensity of various zones between BPNN, SOM, RNN and SVM. Symmetrical and non-symmetrical zoning algorithms were compared as input. 400 different datasets have been tested and the experiments indicate that SVM classification is superior to the other three classifiers. The experiments also indicate that the combination of symmetrical and non-symmetrical zoning design is better than non-symmetrical or symmetrical zoning only.

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“…Neural network has strong ability of learning knowledge and classification and has a high faulttolerance and robustness, complex decision-making region in the arbitrary feature space can be formed, it has the selforganization and self-learning function, so the constraints of the traditional pattern recognition were greatly broadened, these characteristics have contributed to the character pattern recognition. At present, there is not the precedent about CMAC neural network [3] [4] applications in the character recognition, and BP neural network [5] is more commonly used. So, CMAC neural network model was compared with the traditional BP neural network model, whose results show that CMAC neural network can effectively identify handwritten character after the character feature was extracted.…”

Section: Introductionmentioning

confidence: 99%

Handwritten Chinese Character Recognition Technology based on CMAC Neural Network

Shen

Liu

2013

Advances in Intelligent Systems Research

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A character recognition technology based on CMAC (Cerebellar Model Articulation Controller) neural network was proposed, considering the neural network with the characteristics of stability and high distinctiveness. The handwriting recognition system interface was designed and developed, which provide a tools for theoretical application. Simulation results showed that the character recognition technology based on CMAC neural network, compared with that based on BP neural network, was accurate in the character recognition rate and character extraction. Besides, the method in the paper can be applied to any similar sign recognition.

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Recognition of printed Chinese characters by using Neural Network

Cited by 14 publications

References 3 publications

Scene Text Recognition using Artificial Neural Network: A Survey

Scene Text Recognition using Artificial Neural Network: A Survey

Document zone content classification for technical document images using Artificial Neural Networks and Support Vector Machines

Handwritten Chinese Character Recognition Technology based on CMAC Neural Network

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