Word recognition is a basic task for intelligent K-12 education, which leads to further complex tasks including grammar checking, composition grading, etc. However, there is little study about recognition of students’ handwritten words. We propose a novel convolutional recurrent neural network architecture that combines attention mechanism with connectionist time classification loss for student handwritten words. And the method also performs excellently in handwritings of adults. With an ablation study, we show that our method is better than its counterpart without attention. The CRNN with attention model we proposed achieve superior performance on word recognition and has the potential to support applications of intelligent K-12 education.
Visual localization determines the position of the viewer in a scene based on his or her viewpoint picture. The problem is challenging because we must handle various view-points in addition to picture quality. In this paper, we present a model that regresses the 6-DOF camera pose from a single RGB image. We use a spatial grid that splits the target space into cells and apply position regression after classifying the viewpoint into a cell. Combining with the adaptive loss and spatial LSTMs, our method outperforms existing approaches by a large margin in both indoor and outdoor scenarios. Furthermore, we present a new integrated indoor and outdoor localization dataset. Results on both public and our datasets show that our method can improve both positional and orientational precision, especially for large scenes.
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