A limited-size ensemble of homogeneous CNN/LSTMs for high-performance word classification

The strength of long short-term memory neural networks (LSTMs) that have been applied is more located in handling sequences of variable length than in handling geometric variability of the image patterns. In this paper, an end-to-end convolutional LSTM neural network is used to handle both geometric variation and sequence variability. The best results for LSTMs are often based on large-scale training of an ensemble of network instances. We show that high performances can be reached on a common benchmark set by using proper data augmentation for just five such networks using a proper coding scheme and a proper voting scheme. The networks have similar architectures (convolutional neural network (CNN): five layers, bidirectional LSTM (BiLSTM): three layers followed by a connectionist temporal classification (CTC) processing step). The approach assumes differently scaled input images and different feature map sizes. Three datasets are used: the standard benchmark RIMES dataset (French); a historical handwritten dataset KdK (Dutch); the standard benchmark George Washington (GW) dataset (English). Final performance obtained for the word-recognition test of RIMES was 96.6%, a clear improvement over other state-of-the-art approaches which did not use a pre-trained network. On the KdK and GW datasets, our approach also shows good results. The proposed approach is deployed in the Monk search engine for historical-handwriting collections

Examining the role of ideological and political education on university students’ civic perceptions and civic participation in Mainland China: Some hints from contemporary citizenship theory

A long existing compulsive curriculum of ideological and political education is employed by the Chinese government to promote citizenship education among Chinese university students. This article builds on the findings of a mixed-methods research that examined the role of ideological and political education on university students’ civic perceptions and civic participation. The results showed little evidence of this curriculum having a clear effect on students’ political participation such as voting, as well as their idealized broad civic participation, but did reveal relatively positive effects on students’ civic intention and civic expression. In addition, it also identified its significant role in organizing students towards attending party-related activities. It shows that ideological and political education is insufficient to achieve specified aims of citizenship education among Chinese university students. We then argue that it results from a mechanistic understanding of citizenship and participation in educational policies and structural barriers to young people’s formal participation. Hence, this article argues that the forms and contents of citizenship education in China need to be reconsidered beyond the limits of the current ideological and political education and that the analyses contributed to an argument for a broader approach to citizenship education to be developed and adopted