Thai Handwritten Recognition on Text Block-Based from Thai Archive Manuscripts

Automatic transcription of ancient handwritten manuscripts can be a challenging task when compared with a transcription of contemporary handwriting. Characters and words can have unusual and varying shapes, with significant variation between writers, and sufficient labelled data from which to train machine learning algorithms can be difficult to access. This paper describes ancient Thai handwriting transcription on block-based from archive manuscripts, using a hybrid deep neural network with both convolutional (CNN) and recurrent (RNN) layers, trained using Connectionist Temporal Classification (CTC) loss. Six architecture variations are compared. Data augmentation was applied to synthetically increase the number of training samples, resulting in improved learning. Thai archive manuscripts were collected from the Thai National Library. The character error rate (CER) in the best architecture was found to be 11.9 percent.