TargetDirected MixUp for Labeling Tangut Characters Deep learning largely improves the performance in computer vision and image understanding tasks depending on large training datasets of labeled images. However, it is usually expensive and timeconsuming to label data although unlabeled data are much easier to get. It is practical to build the training dataset iteratively from a small set of manually labeled data because of the limited budget or emerging new categories. The labeled data could not only be used for training the model but also some knowledge could be mined from them for finding examples of the classes not included in the training dataset. Mixup 1 improves the modelu0027s accuracy and generalization by augmenting the training dataset with the virtual examples that are generated by mixing pairs of randomly selected examples from the training dataset. Motivated by Mixup, we propose the TargetDirected Mixup (TDM) method for building the training dataset of the deep learningbased Tangut character recognition system. The virtual examples are generated by mixing two or more similar examples in the training dataset, together with the target examples of unseen classes that need to be labeled, which is a kind of generative fewshot learning. This method can help expand the training dataset by finding real examples of unseen Tangut characters and provide virtual examples that could represent the rare characters that are used very limited in historical documents. According to our experiments, TDM can help recognize the unseen examples at the accuracy of 80% with only 4 to 5 real target examples, which largely reduces human labor in data annotation.

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We examine the use case of performing handwritten character recognition (HCR) on a newly compiled collection of Tibetan historical documents, which presents multiple challenges, including inherent challenges such as image quality and the lack of word separation, and dataset challenges such as a lack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.