Generative adversarial networks (GANs) has proven hugely successful in variety of applications of image processing. However, generative adversarial networks for handwriting is relatively rare somehow because of difficulty of handling sequential handwriting data by Convolutional Neural Network (CNN). In this paper, we propose a handwriting generative adversarial network framework (HWGANs) for synthesizing handwritten stroke data. The main features of the new framework include: (i) A discriminator consists of an integrated CNN-Long-Short-Term- Memory (LSTM) based feature extraction with Path Signature Features (PSF) as input and a Feedforward Neural Network (FNN) based binary classifier; (ii) A recurrent latent variable model as generator for synthesizing sequential handwritten data. The numerical experiments show the effectivity of the new model. Moreover, comparing with sole handwriting generator, the HWGANs synthesize more natural and realistic handwritten text.
: The GANs proposed on 2019 @cite_1 aims at generating realistic images of handwritten texts, which is naturally a fit for Optical Character Recognition (OCR). The authors use bidirectional LSTM recurrent layers to get an embedding of the word to be rendered, and then feed it to the generator network. They also modify the standard GAN by adding an auxiliary network for text recognition. However, since this approach can not directly synthesize handwritten text of digital ink, although its generated images are realistic, an additional effective Ink Grab algorithm is further required for the conversion from image to digital stroke.
@cite_1
2920553990 2009444210 2752225195 2573871018
State-of-the-art offline handwriting text recognition systems tend to use neural networks and therefore require a large amount of annotated data to be trained. In order to partially satisfy this requirement, we propose a system based on Generative Adversarial Networks (GAN) to produce synthetic images of handwritten words. We use bidirectional LSTM recurrent layers to get an embedding of the word to be rendered, and we feed it to the generator network. We also modify the standard GAN by adding an auxiliary network for text recognition. The system is then trained with a balanced combination of an adversarial loss and a CTC loss. Together, these extensions to GAN enable to control the textual content of the generated word images. We obtain realistic images on both French and Arabic datasets, and we show that integrating these synthetic images into the existing training data of a text recognition system can slightly enhance its performance. Recurrent neural networks (RNN) have been successfully applied for recognition of cursive handwritten documents, both in English and Arabic scripts. Ability of RNNs to model context in sequence data like speech and text makes them a suitable candidate to develop OCR systems for printed Nabataean scripts (including Nastaleeq for which no OCR system is available to date). In this work, we have presented the results of applying RNN to printed Urdu text in Nastaleeq script. Bidirectional Long Short Term Memory (BLSTM) architecture with Connectionist Temporal Classification (CTC) output layer was employed to recognize printed Urdu text. We evaluated BLSTM networks for two cases: one ignoring the character's shape variations and the second is considering them. The recognition error rate at character level for first case is 5.15 and for the second is 13.6 . These results were obtained on synthetically generated UPTI dataset containing artificially degraded images to reflect some real-world scanning artifacts along with clean images. Comparison with shape-matching based method is also presented. Optical Character Recognition (OCR) aims to recognize text in natural images. Inspired by a recently proposed model for general image classification, Recurrent Convolution Neural Network (RCNN), we propose a new architecture named Gated RCNN (GRCNN) for solving this problem. Its critical component, Gated Recurrent Convolution Layer (GRCL), is constructed by adding a gate to the Recurrent Convolution Layer (RCL), the critical component of RCNN. The gate controls the context modulation in RCL and balances the feed-forward information and the recurrent information. In addition, an efficient Bidirectional Long Short-Term Memory (BLSTM) is built for sequence modeling. The GRCNN is combined with BLSTM to recognize text in natural images. The entire GRCNN-BLSTM model can be trained end-to-end. Experiments show that the proposed model outperforms existing methods on several benchmark datasets including the IIIT-5K, Street View Text (SVT) and ICDAR. Recently, we propose deep neural network based hidden Markov models (DNN-HMMs) for offline handwritten Chinese text recognition. In this study, we design a novel writer code based adaptation on top of the DNN-HMM to further improve the accuracy via a customized recognizer. The writer adaptation is implemented by incorporating the new layers with the original input or hidden layers of the writer-independent DNN. These new layers are driven by the so-called writer code, which guides and adapts the DNN-based recognizer with the writer information. In the training stage, the writer-aware layers are jointly learned with the conventional DNN layers in an alternative manner. In the recognition stage, with the initial recognition results from the first-pass decoding with the writer-independent DNN, an unsupervised adaptation is performed to generate the writer code via the cross-entropy criterion for the subsequent second-pass decoding. The experiments on the most challenging task of ICDAR 2013 Chinese handwriting competition show that our proposed adaptation approach can achieve consistent and significant improvements of recognition accuracy over a highperformance writer-independent DNN-HMM based recognizer across all 60 writers, yielding a relative character error rate reduction of 23.62 in average.