Thai Handwritten Recognition on Text BlockBased 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 blockbased 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.

Transductive Learning for Reading Handwritten Tibetan Manuscripts We examine the use case of performing handwritten character recognition (HCR) on a newly compiled collection of Tibetan historical documents, which presents multiple challenges, including inherent challenges such as image quality and the lack of word separation, and dataset challenges such as a lack of supervised training data. To tackle these challenges, we introduce an endtoend unsupervised fulldocument HCR approach composed of unsupervised line segmentation and a convolutional recurrent neural network, trained using solely synthetic data. Various augmentations are applied to these synthesized images, and we compare the effect of each augmentation on the HCR results. Since we work on a collection of historical manuscripts, we can fit the model to the available test data. During training, our network has access to both the labeled synthetic training data and the unlabeled images of the test set, and we adapt and evaluate four different semisupervised learning and domain adaptation approaches for transductive learning in HCR. We test our approach on a set of 167 images from the Kadam collection, containing 829 lines. We show that correct data augmentation is crucial for the success of HCR trained solely on synthetic data and that using an effective transductive learning approach drastically improves results.