This research attempts to construct a network that can convert online and offline handwritten characters to each other. The proposed network consists of two Variational Auto-Encoders (VAEs) with a shared latent space. The VAEs are trained to generate online and offline handwritten Latin characters simultaneously. In this way, we create a cross-modal VAE (Cross-VAE). During training, the proposed Cross-VAE is trained to minimize the reconstruction loss of the two modalities, the distribution loss of the two VAEs, and a novel third loss called the space sharing loss. This third, space sharing loss is used to encourage the modalities to share the same latent space by calculating the distance between the latent variables. Through the proposed method mutual conversion of online and offline handwritten characters is possible. In this paper, we demonstrate the performance of the Cross-VAE through qualitative and quantitative analysis.
For offline and online handwriting conversion, it has traditionally been done using classical feature-based methods @cite_17 but there has been some recent work using neural networks. @cite_9 used a CNN and RNN-based Encoder-Decoder network for handwriting trajectory recovery. Attempts were also made using neural networks to identify graph features @cite_20 and for sequential stroke prediction using regression CNNs @cite_21 .
@cite_9 @cite_21 @cite_20 @cite_17
2964103813 2902343479 2096252661
Pen Tip Motion Prediction (PTMP) is the key step for Chinese handwriting order recovery (DOR), which is a challenge topic in the past few decades. We proposed a novel algorithm framework using Convolutional Neural Network (CNN) to predict pen tip movement for human handwriting pictures. The network is a regression CNN model, whose inputs are a series of part-drawn handwriting images and output is a vector that represents the probability of next stroke point position. The predicted output vector is utilized by an iteration framework to generate pen movement sequences. Experiments on public Chinese and English online handwriting database have indicated that the proposed model performs competitively in multi-writer handwriting PTMP and DOR tasks. Furthermore, the experiment demonstrated that characters belong to different languages shares some common writing patterns and the proposed method could learn these laws effectively. This paper proposes a novel method for offline text-independent writer identification by using convolutional neural network (CNN) and joint Bayesian, which consists of two stages, i.e. feature extraction and writer identification. In the stage of feature extraction, since a large number of data is essential to train an effective CNN model with high generalizability and the amount of handwriting is limited in writer identification, a data augmentation technique is first developed to generate thousands of handwriting images for each writer. Then a deep CNN network is designed to extract discriminative features to represent the properties of different writing styles, which is trained by using the generated handwriting images. In the stage of writer identification, the training dataset is used to train the CNN model for feature extraction and the joint Bayesian technique is employed to accomplish the task of writer identification based on the extracted CNN features. The proposed method is tested on two standard benchmark datasets, i.e. ICDAR2013 and CVL dataset. Experimental results demonstrate that the proposed method gets the best performance compared to the state-of-the-art approaches. In a document network such as a citation network of scientific documents, web-logs, etc., the content produced by authors exhibits their interest in certain topics. In addition some authors influence other authors' interests. In this work, we propose to model the influence of cited authors along with the interests of citing authors. Moreover, we hypothesize that apart from the citations present in documents, the context surrounding the citation mention provides extra topical information about the cited authors. However, associating terms in the context to the cited authors remains an open problem. We propose novel document generation schemes that incorporate the context while simultaneously modeling the interests of citing authors and influence of the cited authors. Our experiments show significant improvements over baseline models for various evaluation criteria such as link prediction between document and cited author, and quantitatively explaining unseen text. A citing sentence is one that appears in a scientific article and cites previous work. Citing sentences have been studied and used in many applications. For example, they have been used in scientific paper summarization, automatic survey generation, paraphrase identification, and citation function classification. Citing sentences that cite multiple papers are common in scientific writing. This observation should be taken into consideration when using citing sentences in applications. For instance, when a citing sentence is used in a summary of a scientific paper, only the fragments of the sentence that are relevant to the summarized paper should be included in the summary. In this paper, we present and compare three different approaches for identifying the fragments of a citing sentence that are related to a given target reference. Our methods are: word classification, sequence labeling, and segment classification. Our experiments show that segment classification achieves the best results.