Recent deep learning based approaches have achieved great success on handwriting recognition. Chinese characters are among the most widely adopted writing systems in the world. Previous research has mainly focused on recognizing handwritten Chinese characters. However, recognition is only one aspect for understanding a language, another challenging and interesting task is to teach a machine to automatically write (pictographic) Chinese characters. In this paper, we propose a framework by using the recurrent neural network (RNN) as both a discriminative model for recognizing Chinese characters and a generative model for drawing (generating) Chinese characters. To recognize Chinese characters, previous methods usually adopt the convolutional neural network (CNN) models which require transforming the online handwriting trajectory into image-like representations. Instead, our RNN based approach is an end-to-end system which directly deals with the sequential structure and does not require any domain-specific knowledge. With the RNN system (combining an LSTM and GRU), state-of-the-art performance can be achieved on the ICDAR-2013 competition database. Furthermore, under the RNN framework, a conditional generative model with character embedding is proposed for automatically drawing recognizable Chinese characters. The generated characters (in vector format) are human-readable and also can be recognized by the discriminative RNN model with high accuracy. Experimental results verify the effectiveness of using RNNs as both generative and discriminative models for the tasks of drawing and recognizing Chinese characters.

Drawing and Recognizing Chinese Characters with Recurrent Neural Network

Background and Objective The Coronavirus 2019, or shortly COVID-19, is a viral disease that causes serious pneumonia and impacts our different body parts from mild to severe depending on patient’s immune system. This infection was first reported in Wuhan city of China in December 2019, and afterward, it became a global pandemic spreading rapidly around the world. As the virus spreads through human to human contact, it has affected our lives in a devastating way, including the vigorous pressure on the public health system, the world economy, education sector, workplaces, and shopping malls. Preventing viral spreading requires early detection of positive cases and to treat infected patients as quickly as possible. The need for COVID-19 testing kits has increased, and many of the developing countries in the world are facing a shortage of testing kits as new cases are increasing day by day. In this situation, the recent research using radiology imaging (such as X-ray and CT scan) techniques can be proven helpful to detect COVID-19 as X-ray and CT scan images provide important information about the disease caused by COVID-19 virus. The latest data mining and machine learning techniques such as Convolutional Neural Network (CNN) can be applied along with X-ray and CT scan images of the lungs for the accurate and rapid detection of the disease, assisting in mitigating the problem of scarcity of testing kits. Methods Hence a novel CNN model called CoroDet for automatic detection of COVID-19 by using raw chest X-ray and CT scan images have been proposed in this study. CoroDet is developed to serve as an accurate diagnostics for 2 class classification (COVID and Normal), 3 class classification (COVID, Normal, and non-COVID pneumonia), and 4 class classification (COVID, Normal, non-COVID viral pneumonia, and non-COVID bacterial pneumonia). Results The performance of our proposed model was compared with ten existing techniques for COVID detection in terms of accuracy. A classification accuracy of 99.1% for 2 class classification, 94.2% for 3 class classification, and 91.2% for 4 class classification was produced by our proposed model, which is obviously better than the state-of-the-art-methods used for COVID-19 detection to the best of our knowledge. Moreover, the dataset with x-ray images that we prepared for the evaluation of our method is the largest datasets for COVID detection as far as our knowledge goes. Conclusion The experimental results of our proposed method CoroDet indicate the superiority of CoroDet over the existing state-of-the-art-methods. CoroDet may assist clinicians in making appropriate decisions for COVID-19 detection and may also mitigate the problem of scarcity of testing kits.

CoroDet: A deep learning based classification for COVID-19 detection using chest X-ray images.

Online and offline handwritten Chinese character recognition: A comprehensive study and new benchmark

Improving handwritten Chinese text recognition using neural network language models and convolutional neural network shape models

Building fast and compact convolutional neural networks for offline handwritten Chinese character recognition

Because of the various appearance (different writers, writing styles, noise, etc.), the handwritten character recognition is one of the most challenging task in pattern recognition. Through decades of research, the traditional method has reached its limit while the emergence of deep learning provides a new way to break this limit. In this paper, a CNN-based handwritten character recognition framework is proposed. In this framework, proper sample generation, training scheme and CNN network structure are employed according to the properties of handwritten characters. In the experiments, the proposed framework performed even better than human on handwritten digit (MNIST) and Chinese character (CASIA) recognition. The advantage of this framework is proved by these experimental results.

Beyond human recognition: A CNN-based framework for handwritten character recognition

It is well known that the handwritten Chinese text recognition is a difficult problem since there are a large number of classes. In order to solve this problem, we proposed a whole new framework for unconstrained handwritten Chinese text recognition. The core module of the framework is the heterogeneous CNN trained by deep knowledge. The experimental results showed that our proposed method could achieve much better performance than the state-of-the-art methods (96.28% vs. 91.39% of CR on CASIA test set). Moreover, since the proposed framework is general, it can also be applied to other time sequence problems, such as speech recognition and video analysis.

Deep Knowledge Training and Heterogeneous CNN for Handwritten Chinese Text Recognition

A training method and a training apparatus for a neutral network for image recognition are provided. The method includes: representing a sample image as a point set in a high-dimensional space, a size of the high-dimensional space being a size of space domain of the sample image multiplied by a size of intensity domain of the sample image; generating a first random perturbation matrix having a same size as the high-dimensional space; smoothing the first random perturbation matrix; perturbing the point set in the high-dimensional space using the smoothed first random perturbation matrix to obtain a perturbed point set; and training the neutral network using the perturbed point set as a new sample. With the training method and the training apparatus, classification performance of a conventional convolutional neural network is improved, thereby generating more training samples, reducing influence of overfitting, and enhancing generalization performance of the convolutional neural network.

Training method and apparatus for neutral network for image recognition

The success of convolutional neural networks (CNNs) in the field of image recognition suggests that local connectivity is one of the key issues to exploit the prior information of structured data. But the problem of selecting optimal local receptive field still remains. We argue that the best way to select optimal local receptive field is to let CNNs learn how to choose it. To this end, we first use different sizes of local receptive fields to produce several sets of feature maps, then an element-wise max pooling layer is introduced to select the optimal neurons from these sets of feature maps. A novel training process ensures that each neuron of the model has the opportunity to be fully trained. The results of the experiments on handwritten Chinese character recognition show that the proposed method significantly improves the performance of traditional CNNs.

Adaptive Local Receptive Field Convolutional Neural Networks for Handwritten Chinese Character Recognition

Recently, the deep learning methods have achieved great success in pattern recognition tasks. Especially for character recognition, most of the state-of-the-art results belong to the deep learning models. Among those models, the convolutional neural network (CNN) becomes the most popular due to its outstanding performance. Therefore, many trials were made in order to make improvements on CNN. However, most of the trials only focused on the network structure or training skills, the inter-class information is usually ignored. In this paper, we have proposed a novel CNN model with two training feedbacks: the reconstruction feedback and the classification feedback. By using the reconstruction feedback, the inter-class information (for example, shape similarity) of the characters is taken into account. Consequently, without enlarging the network structure, our model can outperform those state-of-the-art improved CNN models, which is proved by the experimental results.

Li Chen

Papers

Beyond human recognition: A CNN-based framework for handwritten character recognition

Deep Knowledge Training and Heterogeneous CNN for Handwritten Chinese Text Recognition

Training method and apparatus for neutral network for image recognition

Adaptive Local Receptive Field Convolutional Neural Networks for Handwritten Chinese Character Recognition

Reconstruction combined training for convolutional neural networks on character recognition