text: 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.
  @cite_9: In this paper, we introduce a novel technique to recover the pen trajectory of offline characters which is a crucial step for handwritten character recognition. Generally, online acquisition approach has more advantage than its offline counterpart as the online technique keeps track of the pen movement. Hence, pen tip trajectory retrieval from offline text can bridge the gap between online and offline methods. Our proposed framework employs sequence to sequence model which consists of an encoder-decoder LSTM module. The proposed encoder module consists of Convolutional LSTM network, which takes an offline character image as the input and encodes the feature sequence to a hidden representation. The output of the encoder is fed to a decoder LSTM and we get the successive coordinate points from every time step of the decoder LSTM. Although the sequence to sequence model is a popular paradigm in various computer vision and language translation tasks, the main contribution of our work lies in designing an end-to-end network for a decade old popular problem in document image analysis community. Tamil, Telugu and Devanagari characters of LIPI Toolkit dataset are used for our experiments. Our proposed method has achieved superior performance compared to the other conventional approaches.
  @cite_21: 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.
  @cite_20: Restoration of writing order from a single-stroked handwriting image can be seen as the problem of finding the smoothest path in its graph representation. In this paper, a 3-phase approach to restore a writing order is proposed within the framework of the edge continuity relation (ECR). In the initial, local phase, in order to obtain possible ECRs at an even-degree node, a neural network is used for the node of degree 4 and a theoretical approach is presented for the node of degree higher than 4 by introducing certain reasonable assumptions. In the second phase, we identify double-traced lines by employing maximum weighted matching. This makes it possible to transform the problem of obtaining possible ECRs at odd-degree node to that at even-degree node. In the final, global phase, we find all the candidates of single-stroked paths by depth first search and select the best one by evaluating SLALOM smoothness. Experiments on static images converted from online data in the Unipen database show that our method achieves a restoration rate of 96.0 percent
  @cite_17: 
summary: 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 .