An efficient character recognition scheme based on k-means clustering

Pourmohammad, Sajjad; Soosahabi, Reza; Maida, Anthony S.

doi:10.1109/icmsao.2013.6552640

Cited by 9 publications

(3 citation statements)

References 11 publications

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“…K -means clustering provides a simple and flexible technique in grouping image intensities. It gives a good performance even in dark images and operates at a low computational complexity since it reduces the data dimension (Pourmohammad, Soosahabi & Maida, 2013). It chooses k centers so as to minimize the total squared distance between each point and its closest center.…”

Section: Collecting Baybayin Datasetmentioning

confidence: 99%

Optical character recognition system for Baybayin scripts using support vector machine

Pino

Mendoza

Sambayan

2021

PeerJ Computer Science

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In 2018, the Philippine Congress signed House Bill 1022 declaring the Baybayin script as the Philippines’ national writing system. In this regard, it is highly probable that the Baybayin and Latin scripts would appear in a single document. In this work, we propose a system that discriminates the characters of both scripts. The proposed system considers the normalization of an individual character to identify if it belongs to Baybayin or Latin script and further classify them as to what unit they represent. This gives us four classification problems, namely: (1) Baybayin and Latin script recognition, (2) Baybayin character classification, (3) Latin character classification, and (4) Baybayin diacritical marks classification. To the best of our knowledge, this is the first study that makes use of Support Vector Machine (SVM) for Baybayin script recognition. This work also provides a new dataset for Baybayin, its diacritics, and Latin characters. Classification problems (1) and (4) use binary SVM while (2) and (3) apply the multiclass SVM classification. On average, our numerical experiments yield satisfactory results: (1) has 98.5% accuracy, 98.5% precision, 98.49% recall, and 98.5% F1 Score; (2) has 96.51% accuracy, 95.62% precision, 95.61% recall, and 95.62% F1 Score; (3) has 95.8% accuracy, 95.85% precision, 95.8% recall, and 95.83% F1 Score; and (4) has 100% accuracy, 100% precision, 100% recall, and 100% F1 Score.

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Section: Collecting Baybayin Datasetmentioning

confidence: 99%

Optical character recognition system for Baybayin scripts using support vector machine

Pino

Mendoza

Sambayan

2021

PeerJ Computer Science

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“…In the proposed method, we have done wide trials on many character dataset of Ashok Scripts, Kadamba, Scripts, Hoysala Scripts and Mysuru Scripts. An efficient system proposed by authors for character recognition by using LDA, PCA and k-means clustering, decreases the needless information in the training data and increases the performance of the system (1) . In (2) pre sented k-means clustering method for recognizing printed Kannada document, which presents a natural grade of font individuality and used to decrease the training dataset's size and got good accuracy.…”

Section: Introductionmentioning

confidence: 99%

OCR for historical Kannada documents using clustering methods

Ravi¹

2020

IJST

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Motivation: In India, the Language Kannada is an ancient and official language in Karnataka State. The study of ancient Kannada scripts from stone carvings, leaf, metal, cloth, paper and other sources enhances our knowledge on the traditions and culture practiced in Karnataka. Due to Poor Quality, variability and the contrast, the Kannada ancient scripts become very challenging to extract the information or to recognize the characters. Objectives: To design a suitable Optical Character Recognition (OCR) technique to read ancient Kannada scripts. Method: Clustering by fast search and find of density peaks is a state-of-the-art density-based clustering algorithm that can effectively find clusters with arbitrary shapes. However, it requires to calculate the distances between all the points in a data set to determine the density and separation of each point. Consequently, its computational cost is extremely high in the case of large-scale data sets. In this work the given document is preprocessed. The features alike SIFT and SURF are extracted and clustered using K-Means clustering. The similarity is computed using different measures. Findings: The classification accuracy was studied under different clustering methods like Kmeans, Agglomerative, Density based clustering with distance based measures like Euclidean and Manhattan. To evaluate the performance of the proposed method, we created our own database of Ashok, Kadamba, Hoysala and Mysuru scripts and experiment was conducted in a database of 4 classes under 70, 50 and 30 different training models from each class. Novelty: We propose a K-means clustering using SIFT and SURF for Kannada ancient manuscript.

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“…Для кластеризации рукописных цифр часто применяются подходы c-means [4] и k-means [5,6]. В работе [7] рукописные письма были сгруппированы с использованием нейронной сети Кохонена.…”

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Применение Технологии CUDA Для Обучения Нейронной Сети Кохонена

Латыпова¹,

Latypova²,

Тумаков³

et al. 2022

ППС

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Проведена кластеризация данных, содержащихся в обучающих выборках -базах данных MNIST и Fashion MNIST. Для кластеризации использована нейронная сеть Кохонена с евклидовой метрикой при оценке расстояний. Для каждой рукописной цифры (MNIST) и каждого типа предметов обихода (Fashion MNIST) определено оптимальное количество кластеров (не более 50).Обучение нейронной сети распараллелено на графическом устройстве NVidia с использованием технологии CUDA. Для каждой цифры приведены результаты, иллюстрирующие сравнение времен работы процессора и графического процессора. Как для цифр, так и для типов предметов обихода сделан вывод о 17-кратном ускорении параллельных вычислений над последовательными на игровом ноутбуке начального уровня. Тестовые выборки из тех же баз данных использованы для проверки правильности построения кластеров. Как для последовательного, так и для параллельного обучения сделан вывод о том, что векторы из тестовой выборки принадлежат правильному кластеру с вероятностью более 90 % в случае рукописных цифр. Кроме того, для каждой цифры и каждого типа предметов обихода вычислены F-меры для оценки попадания объектов в свои кластеры.Показано, что последовательная и параллельная кластеризации дают близкие результаты. Наилучшие значения F-меры получены для цифр 0 и 1 (F-среднее значение равно 0,974), в то время как худшее значение (0,903) получено для цифры 9. Для данных Fashion MNIST лучшее значение F-меры (0,96) получено для типа «Брюки», а худшее (0,34) -для типа «Рубашка». Несмотря на большие разбросы для значений F-метрик двух рассмотренных баз данных, отличия результатов кластеризации минимальны. Так, для MNIST максимальное отличие F-меры составляет порядка 0,01, а для Fashion MNISTоколо 0,04.

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An efficient character recognition scheme based on k-means clustering

Cited by 9 publications

References 11 publications

Optical character recognition system for Baybayin scripts using support vector machine

Optical character recognition system for Baybayin scripts using support vector machine

OCR for historical Kannada documents using clustering methods

Применение Технологии CUDA Для Обучения Нейронной Сети Кохонена

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