A Multilingual Handwritten Character Dataset: T-H-E Dataset

Bartos, Gaye Ediboglu; Hoşcan, Yaşar; Kauer, András; Hajnal, Éva

doi:10.12700/aph.17.9.2020.9.8

Cited by 6 publications

(2 citation statements)

References 23 publications

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“…The multilingual Turkish-Hungarian-English T-H-E Dataset development and recognition of characters are presented by Gaye Edibo˘glu Bartos et al (2020). LeNet a deep learning model is used to confirm the validity of the dataset.…”

Section: A Dataset Buildingmentioning

confidence: 99%

Comprehensive Dataset Building and Recognition of Isolated Handwritten Kannada Characters Using Machine Learning Models

Hebbi

Mamatha

2023

AIA

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In this work, an attempt is made to build a dataset for handwritten Kannada characters and also to recognize the isolated Kannada vowels, consonants, modifiers, and ottaksharas. The dataset is collected from 500 writers of varying ages, gender, qualification, and profession. This dataset will be used to recognize the handwritten kagunita's, ottaksharas, and other base characters, where the existing works have addressed very less on the recognition of kagunita's and ottaksharas. There are no datasets for the same. Hence, a dataset for handwritten 85 characters is built using an unsupervised machine learning technique i.e K-means hierarchical clustering with Run Length Code (RLC) features. An accuracy of 80% was achieved with the unsupervised method. The dataset consists of 130,981 samples for 85 classes, these classes are further divided into upper, lower, and middle zones based on the position of the character in the dialect. After the dataset was built SVM model with HOG features was used for recognition and an accuracy of 99.0%, 88.6%, and 92.2% was obtained for the Upper, Middle, and Lower zones respectively to increase the recognition rate, the CNN model is fine-tuned with raw input, and an accuracy of 100%, 96.15%, and 95.38% was obtained for the Upper, Middle, and Lower zones respectively. With the ResNet18 model, an accuracy of 99.88%, 98.92, and 97.55% was obtained for each of the zones respectively. The dataset will be made available online for the researchers to carry out their research on handwritten characters, kagunitas, and word recognition with segmentation.

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Section: A Dataset Buildingmentioning

confidence: 99%

Comprehensive Dataset Building and Recognition of Isolated Handwritten Kannada Characters Using Machine Learning Models

Hebbi

Mamatha

2023

AIA

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“…Character datasets such as Arabic [9][10][11][12] and Latin [4,13,14] were created from different alphabets and languages to be used in handwriting character recognition. There are handwritten character recognition studies on many languages and alphabets in the literature.…”

Section: Related Workmentioning

confidence: 99%

Comparison of Handwritten Recognition Methods on Arabic and Latin Characters

Tutar

2022

J. Stud. Sci. Eng.

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In this article, both machine learning techniques and deep learning methods were applied on the digit datasets created using the Arabic and Latin alphabets, and the performances of the methods were compared. Each method was tested with various parameters and the results were analyzed. In addition, with this study, the recognizability of handwritten numeral datasets created using different alphabets was also observed. For experiments, an Arabic alphabet handwritten digit dataset (60,000 training and 10,000 testings) and a Latin alphabet handwritten digit dataset (60,000 training and 10,000 testings) were used. When the results of the experiment are examined, it is seen that successful results are obtained in the classification made with the MADBase dataset in some methods and in the classification made with the MNIST dataset in some methods. As a result, it can be stated that the handwriting character recognition success of a method cannot be measured only by the classification made on a dataset. Also, the digits written in the Arabic alphabet appear to be almost more recognizable than the digits written in the Latin alphabet.

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Current and Potential New Methods to Prevent Damage Caused by Wild Animals in Agricultural Areas

Kovács,

Hajnal

2024

2024 IEEE 18th International Symposium on Applied Computational Intelligence and Informatics (SACI)

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A Multilingual Handwritten Character Dataset: T-H-E Dataset

Cited by 6 publications

References 23 publications

Comprehensive Dataset Building and Recognition of Isolated Handwritten Kannada Characters Using Machine Learning Models

Comprehensive Dataset Building and Recognition of Isolated Handwritten Kannada Characters Using Machine Learning Models

Comparison of Handwritten Recognition Methods on Arabic and Latin Characters

Current and Potential New Methods to Prevent Damage Caused by Wild Animals in Agricultural Areas

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