Thammano, Arit
Loading...
Preferred name
Thammano, Arit
Alternative Name
Thammano, A.
Thummano, Arit
Main Affiliation
Email
arit.th@kmitl.ac.th
20 results
Now showing 1 - 10 of 20
- Some of the metrics are blocked by yourconsent settings
Item type:Publication, Printed Thai character recognition using the hybrid approach(2002-01-01); Ruxpakawong, PhongthepMany researchers have been conducted on the recognition of Thai characters. Different approaches, such as neural network, syntactic, and structural methods, have been proposed. However, the success in recognizing Thai characters is still limited, compared to English characters. This paper proposes an approach to recognize the printed Thai characters using the hybrid of global feature, local features, fuzzy membership function and the neural network. The global feature classifies all characters into seven main groups. Then the local features and the neural network are applied to identify the characters. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Dynamic system identification using recurrent neural network with multi-valued connection weight(2009-01-01); Ruxpakawong, PhongthepThis paper introduces a new concept of the connection weight to the standard recurrent neural networks - Elman and Jordan networks. The architecture of the modified networks is the same as that of the original recurrent neural networks. However, in the modified networks the weight of each connection is multi-valued, depending on the value of the input data involved. The backpropagation learning algorithm is also modified to suit the proposed concept. The modified networks have been benchmarked against their original counterparts. The results on eleven benchmark problems are very encouraging. ©2009 IEEE. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Applying a weighting matrix to the hierarchical neural network model for handwritten thai character recognition(2006-01-01); Poolsamran, PatcharawadeeThis paper proposes a new neural network approach to the off-line handwritten Thai character recognition. This new neural network is a hierarchical neural network; it employs the concept of a weighting matrix in measuring the similarity between the incoming input pattern and the reference patterns. The experiments have been conducted to recognize both slipshod and proper handwritten characters. The results demonstrate a very promising performance of the proposed approach. © 2006 IEEE. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Printed thai character recognition using the hierarchical cross-correlation ARTMAP(2005-01-01); Duangphasuk, PruegsaTraditionally, 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. © 2005 IEEE. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Handwritten Thai character recognition using fuzzy membership function and fuzzy ARTMAP(2003-01-01) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Solving classification problems using supervised self-organizing map(2007-12-01); Kiatwuthiamorn, JirapornThis paper proposes the new approach to deal with the classification problems by modifying the well-known Kohonen self-organizing map in order to make it able to solve classification problems. During training, the fuzzy membership function is used in place of the Euclidean distance to find the best matching cluster for the input pattern. In order to improve the efficiency of proposed model, the fuzzy entropy concept is employed to reduce the number of nodes in the cluster layer. The performance of the proposed model was compared with the fuzzy ARTMAP neural network. The results on five benchmark problems are very encouraging. ©2007 IEEE. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Human face recognition using modified Hausdorff ARTMAP(2005-01-01); Ruensuk, SongpolThis paper proposes a new neural network approach specifically designed for solving two dimensional binary image recognition problems. The proposed neural network is an extension of the Hausdorff ARTMAP introduced by Thammano and Rungruang [1]. The objectives of this research are to improve the accuracy and correct the drawbacks of the original network. The performance of this proposed model has been compared with that of the original Hausdorff ARTMAP. The experimental results on two benchmark databases, the ORL and Yale face databases, show that the proposed network surpasses the original Hausdorff ARTMAP in both performance and processing time. © Springer-Verlag Berlin Heidelberg 2005. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, English-Thai example-based machine translation using n-gram model(2006-01-01); Supnithi, ThepchaiThe necessity on exchanging information among countries become a major task in information based society. Machine translation is an application that enables users to communicate each other without language barrier problem. With the great support on computer's efficiency, corpus-based technology becomes a fundamental concept for developing software based on a large amount of data. We introduce the first example-based English to Thai machine translation using n-gram model and implemented the system. Some advantages and disadvantages of this method are discussed. © 2006 IEEE. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, A neural network model for online handwritten mathematical symbol recognition(2006-01-01); Rugkunchon, SukhumalThis paper proposes a new handwritten mathematical symbol recognition system that is flexible enough to let the users write the symbols in their own ways. They do not have to learn a completely new way of writing symbols. The proposed approach involves two main stages: online and offline. During the online stage, the input is classified into one of the four groups. During the offline stage, the new neural network, called Hausdorff ARTMAP, which is specifically designed for solving two dimensional binary pattern recognition problems is used to identify the symbols. The proposed model is tested in a writer independent mode using the researcher's own collected database. The result obtained is very encouraging. © Springer-Verlag Berlin Heidelberg 2006. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Handwritten Thai character recognition using fuzzy linguistic rules(2002-12-01)