# Burapha-TH: A Multi-Purpose Character, Digit, and Syllable Handwriting Dataset
###### Abstract
The SERI95 merged with ETRI, which contains 520 sets, one for each of the most frequently used Hangul characters. Each set contains about a thousand samples. Several datasets of Thai handwriting have been published. [PERSON] and [PERSON] introduced a Thai online and offline handwritten character corpus in 2004 [14]. The online handwritten character corpus contains more than 44,000 handwriting samples. The images were collected using a program developed for a WACOM 6 \(\times\) 8 tablet used by 63 different writers who entered Thai characters, English characters, and special symbols. The characters written include: (1) 79 patterns of Thai consonants, vowels, tones and digit characters; (2) 62 patterns of English uppercase, lowercase, and digit characters; and (3) 15 patterns of special symbols. This offline handwritten character corpus contains handwritten isolated characters, words, and sentences, and 14,000 long samples from 143 different writers. The handwritten isolated character set contains 79 patterns of consonants, vowels, tones, and digits. The word set includes names of 76 Thai provinces and 21 Thai digits. The sentence sets include 16 Thai digits and 3 Thai general articles. A new Thai handwriting dataset, ALICE-THI, was published in 2015 [15]. This dataset was collected to support research on handwritten character recognition using local gradient feature descriptors. ALICE-THI consists of 13,130 training samples and 1360 test samples, 44 consonants, 17 vowels, 4 tones, and 3 symbols. The ALICE-THI handwritten Thai digit dataset contains 8555 training samples and 1000 test samples, for 9555 samples. It is common knowledge that a large dataset, in terms of the number of images, can help achieve better accuracy when using deep learning techniques [16; 17; 18]. The construction of a good handwriting dataset, which is comprehensive, has enough variety and is large enough for suitable training of deep learning algorithms. The lack of a diverse dataset of handwritten Thai scripts is problematic for a researcher working on Thai handwriting recognition. Another problem with existing datasets is that they cannot be easily compared. Therefore, Thai handwriting research is not progressing as quickly as it should. These issues inspired us to construct a new dataset to foster more Thai handwriting recognition research. This paper introduces a new Thai handwriting dataset named Burapha-TH consisting of characters, digits, and syllables. Our dataset is very different from existing standard handwriting datasets. Standard datasets are generally explicit and contain preprocessed images. The preprocessing removes noise, traces image contours, performs smoothing on the images, removes the non-essential background, and performs binarization. In contrast, our new Burapha-TH dataset has only performed de-skewing and segmentation in its preprocessing steps. We did not remove the salt and pepper noise, white background, or artifacts generated by scanning. The objectives when creating this dataset were to provide good opportunities for research on a wide variety of Thai handwriting recognition tasks. The dataset is suitable for research about handwriting recognition, including feature extraction, machine learning [19], deep learning [20], and image processing of handwriting. The dataset contains raw images (without any pre-processing) of each document. We published the original data collection sheets to permit new research on glyph image processing, including alternative preprocessing approaches with more advanced skew correction, line detection, segmentation, image smoothing, and noise and white background removal. The dataset can be used to explore writing patterns related to gender. The researcher can exploit the potential of syllables handwriting style. The syllables can represent a unique style of handwriting. For each syllable, it shows a continuous handwriting style that is suitable for word segmentation or handwriting generation.