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SEACrowd/kvis_th_ocr

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SEACrowd/kvis_th_ocrSEACrowd/kvis_th_ocrcc-by-4.0: declared licencecc-by-4.0declared licenceREADME.md ↗cc-by-4.0: licence filecc-by-4.0licence fileLICENSE ↗
SEACrowd/kvis_th_ocrSEACrowd/kvis_t…cc-by-4.0: declared licencecc-by-4.0declared licenceREADME.md ↗cc-by-4.0: licence filecc-by-4.0licence fileLICENSE ↗
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Citation text from the card

@INPROCEEDINGS{8584876,
  author={Joseph, Ferdin Joe John and Anantaprayoon, Panatchakorn},
  booktitle={2018 International Conference on Information Technology (InCIT)},
  title={Offline Handwritten Thai Character Recognition Using Single Tier Classifier and Local Features},
  year={2018},
  volume={},
  number={},
  pages={1-4},
  abstract={Handwritten character recognition is a conversion process of handwriting into machine-encoded text. Currently,
  several techniques and methods are proposed to enhance accuracy of handwritten character recognition for many languages
  spoken across the globe. In this project, a local feature-based approach is proposed to enhance the accuracy of handwritten
  offline character recognition for Thai alphabets. In the experiment, through MATLAB, 100 images for each class of Thai
  alphabets are collected and k-fold cross validation is applied to manage datasets to train and test. A gradient invariant
  feature set consisting of LBP and shape features is extracted. The classification is operated by using query matching based
  on Euclidean distance. The accuracy would be the percentage of correct classification for each class. For the result, the
  highest accuracy is 68.96% which has 144-bit shape features and uniform pattern LBP for the features.},
  keywords={Character recognition;Feature extraction;Shape;Genetic algorithms;Matlab;Gray-scale;Optical character recognition
  software;Offline Character Recognition;Local Binary Pattern;Thai Ha

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dataset_licence({ "id": "SEACrowd/kvis_th_ocr" })
signal: "permissive"
stated_licence: "cc-by-4.0"
sources: 0