# Burapha-TH: A Multi-Purpose Character, Digit, and Syllable Handwriting Dataset
###### Abstract  
In handwriting recognition research, a public image dataset is necessary to evaluate algorithm correctness and runtime performance. Unfortunately, in existing Thai language script image datasets, there is a lack of variety of standard handwriting types. This paper focuses on a new offline Thai handwriting image dataset named Burapha-TH. The dataset has 68 character classes, 10 digit classes, and 320 syllable classes. For constructing the dataset, 1072 Thai native speakers wrote on collection datasheets that were then digitized using a 300 dpi scanner. De-skewing, detection box and segmentation algorithms were applied to the raw scans for image extraction. The experiment used different deep convolutional models with the proposed dataset. The result shows that the VGG-13 model (with batch normalization) achieved accuracy rates of 95.00%, 98.29%, and 96.16% on character, digit, and syllable classes, respectively. The Burapha-TH dataset, unlike all other known Thai handwriting datasets, retains existing noise, the white background, and all artifacts generated by scanning. This comprehensive, raw, and more realistic dataset will be helpful for a variety of research purposes in the future. T 202220013 April 2022218 April 20222  
of the English alphabet. This dataset was introduced in 2017 and is extensively used to improve deep learning algorithms. There are several scripts that have been proposed as contributions to an international standard handwritten character dataset. The Institute of Automation of the Chinese Academy of Sciences (CASIA) [9] released CASIA-HWDB (offline) and CASIA-OLHWDB (online) in 2011. These datasets contain offline/online handwritten characters and continuous text written by 1020 people using Anoto pens on paper. The datasets of isolated characters contain about 3.9 million samples of 7185 Chinese characters and 171 symbols, and the datasets of handwritten texts contain about 5090 pages with 1.35 million character samples. The National Institute of Japanese Literature released the Kuzushiji dataset in November 2016 [10]. They scanned 35 classical books printed in the 18 th century and organized their proposed dataset into three parts: (1) Kuzushiji-MNIST, a drop-in replacement for the MNIST dataset; (2) Kuzushiji-49, a much larger, but imbalanced dataset containing 48 Hiragana characters and one Hiragana iteration mark; and (3) Kuzushiji-Kanji, an imbalanced dataset of 3832 Kanji characters, including rare characters with very few samples. The Kuzushiji dataset currently consists of 3999 character types and 403,242 characters. A public domain handwritten character image dataset for the Malayalam language script contains data provided by 77 native Malayalam writers. It includes independent vowels, consonants, half consonants, vowels, consonant modifiers, and conjunct characters [11]. The glyphs of the Malayalam script have 85 classes that contain 17,236 training images, 5706 validation images, and 6360 testing images. An active contour model-based minimization technique was applied for character segmentation. The dataset was evaluated with different feature extraction techniques. A scattering convolutional network achieves 91.05% recognition accuracy. The PE-92 database project was started in 1992. PE-92 contains 100 image sets of 2350 Korean characters, considered general and in daily use [12; 13]. The handwritten Korean language is syllable-based, not alphabet-based like western European languages. One person wrote between 100 and 500 characters for each set in the first 70 images sets. This dataset tries to accumulate as many writing styles as possible. Some problems occurred while developing the database in the data collection process, even though the characters selected to be written were considered general ones. For example, misspelling of complex vowels was often found. In 1997, SERI (System Engineering Research Institute) of Korea University created SERI95, a Hangul dataset.