Recognition of handprinted Thai characters using the cavity features of character based on neural network

Phokharatkul, Pisit; Kimpan, Chom

doi:10.1109/apccas.1998.743689

Cited by 21 publications

(9 citation statements)

References 3 publications

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“…Image normalization is very important in the extraction process, based on the study of the Thai character recognition system research in [3] and [4], the normalization of Thai characters to the size of 64 x 64 pixels will mostly achieve the high performance rates in their system. Therefore, our system adopted the same size normalization of those researches.…”

Section: ) Image Normalizationmentioning

confidence: 99%

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Thai handwritten character recognition using heuristic rules hybrid with neural network

Mitrpanont

Imprasert

2011

2011 Eighth International Joint Conference on Computer Science and Software Engineering (JCSSE)

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This research enhanced two major processes of the previous work of the off-line Thai handwritten character recognition using hybrid techniques of heuristic rules and neural network system. The proposed functions are mainly in 1) Feature extraction enhancement to improve the feature conflict resolution rule and the specialized neural network-based zigzag feature extraction. These functions are used to refine the conflict features and zigzag patterns; 2) Neural network-based recognition. Specifically, a neural network technique improves the capabilities of the recognition process to handle various styles of writing. The result showed that the additional feature conflict resolution rule could achieve the feature extraction rate of 87.85% (increased 2.13%), the feature extraction rate of the specialized neural network-based zigzag extraction could achieve 90.48% (increased 47.9%) and the recognition rate of the neural network-based recognition which combine both of the two proposed feature extraction functions could achieve 92.78% (increased 9.77%).

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Section: ) Image Normalizationmentioning

confidence: 99%

“…Finally, the encoded value of the extracted features is fed to our neural network for training and recognition, respectively. Backpropagation neural network is widely applied to several character recognition researches such as [6], [7], [8], [9] and [10].…”

Section: ) Neural Network Training and Recognitionmentioning

confidence: 99%

Thai handwritten character recognition using heuristic rules hybrid with neural network

Mitrpanont

Imprasert

2011

2011 Eighth International Joint Conference on Computer Science and Software Engineering (JCSSE)

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“…However, most of the researches put a lot of effort into the recognition of standard Thai characters. The methods widely used are the statistical approach [1,2], the neural network based approach [3,4,5], and the hybrid approach [6,7]. In this paper, the hierarchical cross-correlation ARTMAP is proposed to recognize the no-head Thai characters.…”

Section: Standard Thai Charactersmentioning

confidence: 99%

“…The weight of this new node is initialized to be equal to the input pattern. s s J I w (5) Next, the weight vector of the winning node is transmitted to the next layer. The choice function of each k th node in the cluster layer is then evaluated as follows: .…”

Section: The Proposed Modelmentioning

confidence: 99%

Printed Thai character recognition using the hierarchical cross-correlation ARTMAP

Thammano

Duangphasuk

2005

17th IEEE International Conference on Tools With Artificial Intelligence (ICTAI'05)

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Traditionally, Thai characters are composed of circle, zigzag line, curve, and head. However, many new Thai fonts, which are now gaining in popularity, do not follow the traditional writing rule; the head has been omitted from the characters. Without the head, it is very difficult to segregate the characters. Even the best commercial Thai OCR software has difficulty in recognizing this kind of character. Therefore, the hierarchical cross-correlation ARTMAP is proposed in this paper to recognize the no-head Thai characters.

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“…Add to that, many documents written in Thai can sometimes contain A typical character recognition system, however coupling might it be, can be separated into two tasks: a feature extraction and a classification. For the classification, there are published papers utilizing Hidden Markov Model [1], [2], Artificial Neuron Network [3], [4], [5], [6], [7], fuzzy rough set [8], Karhunen-Loeve expansion [9], as well as a hybrid approach [10]. Evidently, there are vast varieties of classification tools which can be used for an OCR system.…”

Section: Introductionmentioning

confidence: 99%

Mixed Thai-English Character Classification Based on Histogram of Oriented Gradient Feature

Siriteerakul

2013

2013 12th International Conference on Document Analysis and Recognition

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The task of classifying mixed Thai-English characters carries considerable challenges due to the number and complexity of the characters. This paper proposes and empirically investigates the performance of a classification system that uses Histogram of Oriented Gradient as an image feature with Support Vector Machine as a classification tool. The experiments were done on the datasets provided by NECTEC which consists of over 600,000 printed images of individual characters from 142 distinct classes. With this proposed method, an accuracy of 97% can be achieved without a look up dictionary or any post-processing system.

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Recognition of handprinted Thai characters using the cavity features of character based on neural network

Cited by 21 publications

References 3 publications

Thai handwritten character recognition using heuristic rules hybrid with neural network

Thai handwritten character recognition using heuristic rules hybrid with neural network

Printed Thai character recognition using the hierarchical cross-correlation ARTMAP

Mixed Thai-English Character Classification Based on Histogram of Oriented Gradient Feature

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