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Offline handwritten text recognition is still a big challenging problem due to various backgrounds, noises, diversity of writing styles, and multiple touches between characters. In this paper, we propose a model of 2D Self-Attention Convolutional Recurrent Network (2D-SACRN) for recognizing handwritten text lines. The 2D-SACRN model consists of three main components: 1) a 2D self-attention based convolutional feature extractor that extracts a feature sequence from an input image; 2) a recurrent encoder that encodes the feature sequence into a sequence of label probabilities; and 3) a CTC-decoder that decodes the sequence of label probabilities into the final label sequence. In this model, we present a 2D self-attention mechanism in the feature extractor to capture the relationships between widely separated spatial regions in an input image. In the experiment, we evaluate the performance of the proposed model on the three datasets: IAM Handwriting, Rimes, and TUAT Kondate. The experimental results show that the proposed model achieves similar or better accuracy when compared to state-of-the-art models in all datasets.

In this paper, we propose an RNN-Transducer model for recognizing Japanese and Chinese offline handwritten text line images. As far as we know, it is the first approach that adopts the RNN-Transducer model for offline handwritten text recognition. The proposed model consists of three main components: a visual feature encoder that extracts visual features from an input image by CNN and then encodes the visual features by BLSTM; a linguistic context encoder that extracts and encodes linguistic features from the input image by embedded layers and LSTM; and a joint decoder that combines and then decodes the visual features and the linguistic features into the final label sequence by fully connected and softmax layers. The proposed model takes advantage of both visual and linguistic information from the input image. In the experiments, we evaluated the performance of the proposed model on the two datasets: Kuzushiji and SCUT-EPT. Experimental results show that the proposed model achieves state-of-the-art performance on all datasets.

The Wartegg Zeichen Test (WZT) is a method of personality evaluation developed by the psychologist Ehrig Wartegg. Three new scoring categories for the WZT consist of Evocative Character, Form Quality, and Affective Quality. In this paper, we present the object detection model in scoring the Affective Quality of WZT. Our works consist of two main parts: 1) using the YOLO (You Only Look Once) model to detect the hand-drawn square boxes from the WZT form, and 2) using YOLO to detect the object in the hand-drawn square box. In the experiments, YOLOv3 achieved 88.94% of mAP for hand-drawn square box detection and 46.90% of mAP for hand-drawn object detection.