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.

DeblurGAN-CNN: Effective Image Denoising and Recognition for Noisy Handwritten Characters

“…The HOGfoDRs+SVM method achieved 98.76% with 5-fold cross-validation. For the updated Thai handwritten dataset, Onuean et al [27] collected Thai handwritten characters, called Burapha-TH, that consisted of 10 digits, 68 characters, and 320 syllable classes. They also created a CNN model using a VGG architecture with a batch normalization layer containing 13 layers, called VGG-13, evaluated on the Burapha-TH dataset, and which achieved 92.29%, 95.00%, and 96.16% accuracy on the digit, character, and syllable classes, respectively.…”

Ranjana Script Handwritten Character Recognition using CNN

“…Devanagari Optical Character Recognition is one such area that has seen many investigations. There are some publicly available benchmarks handwritten character databases available for scripts like Odia [3], Arabic [4] [5], Malayalam [6] [7], Meitei Mayek [8], Farsi [9], Telugu [10], Urdu [11], Tifinagh [12], Thai [13] and MNIST [14] datasets. However, no research has been carried out on character recognition of the Ranjana script.…”