Recognizing Thai handwritten characters and words for human–computer interaction

Pornpanomchai, Chomtip; Batanov, Dentcho N.; Dimmitt, Nicholas J.

doi:10.1006/ijhc.2001.0466

Cited by 9 publications

(3 citation statements)

References 36 publications

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“…2, the Lanna sentence structure is divided into 3 main levels as in Latin family language. Additionally, Lanna language has 2 sublevels in the upper level [1,2]. The big difference between Lanna and Latin is Lanna does not have space between words.…”

Section: Background Materialsmentioning

confidence: 99%

Lanna Handwritten Character Recognition on Historical Documents Using Feature Extraction

Khankasikam

2012

AMM

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The objective of this paper is to develop methodology that can recognize the Lanna handwritten character on historical documents by using character feature extraction technique. Historical documents are national treasures. Insignificant effort has been made to preserve Lanna historical documents. Other nations such as Egypt, China and Greece are investing a large effort in restoring and preserving their national historical documents. As a starting point, the focus is on using one Lanna historical document for performing experiments and verifying recognition methods available in this research area. The proposed system consists of three modules, which are image preprocessing module, feature extraction module and character recognition module. The details of each module are following: first, the input image is transformed into a suitable image for feature extraction module. Second, the proposed system extracts character features from the image. Finally, the extracted character information, which is kept in form of bit string, is calculated a similarity value for recognition result. The experiment was conducted on more than 4,000 Lanna handwritten characters by using 10-fold cross-validation classification method which is using 3,600 for training characters and 400 for testing character. The cross-validation process is repeated 10 times, with each of the 10 subsets used exactly once as the validation data. The precision of the proposed system is around 89.73 percent.

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Section: Background Materialsmentioning

confidence: 99%

Lanna Handwritten Character Recognition on Historical Documents Using Feature Extraction

Khankasikam

2012

AMM

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“…Researchers have shown that language differences create challenges in interfaces [15,10]. Majority of the users in developing nations cannot read, write, speak English and therefore keypads on mobile phones have to be developed with local/regional languages [8].…”

Section: Usabilitymentioning

confidence: 99%

Challenges and novelties while using mobile phones as ICT devices for Indian masses

Yadav

Naik

Singh

et al. 2010

Proceedings of the 4th ACM Workshop on Networked Systems for Developing Regions

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Mobile phones have emerged as truly pervasive and affordable Information and Communication Technology (ICT) platform in the last decade. Large penetration of cellular networks and availability of advanced hardware platforms have inspired multiple innovative research opportunities in mobile computing domain. However, most of the research challenges have focused on typical scenarios existing in the developed economies. Using the healthcare as an application for mobile computing, we present research challenges and novelties in mobile computing domain that take account for differences between developing such as India and developed economies based on commonly available mobile platforms, communication cost, differences in user behavior and acceptable societal norms, among others. With localization being a critical component of various mobile systems, we present early results for the application of GPS-less localization that highlight the corresponding differences in Indian context.

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“…And this handwriting recognition is plays a crucial role in the human computer interaction model. Efforts have already been made to build system in both online and offline fields for achieving various aims, like recognizing numeric characters, language recognitions like Assamese [2], Thai [5], and Arabic [4].…”

Section: Introductionmentioning

confidence: 99%

Augmentation of Local, Global Feature Analysis for online Character Recognition System for Telugu Language using Feed Forward Neural Networks (FFNN)

Srinivasarao¹,

Ramisetty²

2019

IJITEE

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In this paper, we propose ANN based online handwritten character recognition for Telugu Language. In literature review, it is observed that Size of the database and preprocessing approaches plays prominent role in the recognition performance. Preprocessing techniques like normalization, interpolation, Uniformization, Smoothing, Slant Correction and resampling techniques are performed for better recognition performance. Local features like(x,y)co-ordinates, (x,y) ( , ) 2 2  x  y and the global features like tan() are considered as features for ANN modeling and Classification of 52 Telugu vowels and consonants. Recognition performance is evaluated by augmentation the local, global features and and tan () Features. Theperformance is evaluated in terms of precision, recall and F-measure. Significant Improvement is reported by augmentation andby adopting preprocessing techniques. The database used for the study is HP-online Telugu database.

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Recognizing Thai handwritten characters and words for human–computer interaction

Cited by 9 publications

References 36 publications

Lanna Handwritten Character Recognition on Historical Documents Using Feature Extraction

Lanna Handwritten Character Recognition on Historical Documents Using Feature Extraction

Challenges and novelties while using mobile phones as ICT devices for Indian masses

Augmentation of Local, Global Feature Analysis for online Character Recognition System for Telugu Language using Feed Forward Neural Networks (FFNN)

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