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Implementation of the handwriting synthesis experiments in the paper Generating Sequences with Recurrent Neural Networks by Alex Graves. The implementation closely follows the original paper, with a few slight deviations, and the generated samples are of similar quality to those presented in the paper. Currently, the Hand class must be imported from demo.py. If someone would like to package this project to make it more usable, please contribute.

https://github.com/sjvasquez/handwriting-synthesisTags | handwriting-synthesis handwriting-generation recurrent-neural-networks tensorflow |

Implementation | Python |

License | Public |

Platform | Windows Linux |

The original RNNLIB is hosted at http://sourceforge.net/projects/rnnl while this "fork" is created to repeat results for the online handwriting prediction and synthesis reported in http://arxiv.org/abs/1308.0850. The later by now is Alex Graves's classic paper on LSTM networks showing of what RNN can learn about the structure present in the sequential input. $ cmake -DCMAKE_BUILD_TYPE=Release . $ cmake --build .

A Neural network based, handwriting recognition software who's aim is to have a cursive OCR software. Although it is used in handwriting recognition, it can be used as well for creating Neural Networks and learning of those networks.

An attempt to implement the random handwriting generation portion of Alex Graves' paper. See my blog post at blog.otoro.net for more information.

TensorFlow implementation of Deep Convolutional Generative Adversarial Networks, Variational Autoencoder (also Deep and Convolutional) and DRAW: A Recurrent Neural Network For Image Generation. Deep Convolutional Generative Adversarial Networks produce decent results after 10 epochs using default parameters.

tensorflow draw recurrent-neural-networks gan vaeJIMHR or quot;Java Interactive Mathematical Handwriting Recognizerquot; as the name suggests is the online handwriting recogition system that specializes in mathematical domain. It processes a user's handwriting through mouse or stylus pen and outputs the c

This repository contains the Neural Network (NN) based Speech Synthesis System developed at the Centre for Speech Technology Research (CSTR), University of Edinburgh.Merlin is a toolkit for building Deep Neural Network models for statistical parametric speech synthesis. It must be used in combination with a front-end text processor (e.g., Festival) and a vocoder (e.g., STRAIGHT or WORLD).

merlin speech-synthesis text-to-speech voice-conversion deep-learning theano tensorflow keras neural-networksHANDWRITTEN.js converts typed text to realistic handwriting.

handwritten text handwriting converter handwriter emnist-dataset daniel-font pen pencil extended-mnistThe purpose of this repository is providing the curated list of the state-of-the-art works on the field of Generative Adversarial Networks since their introduction in 2014. You can also check out the same data in a tabular format with functionality to filter by year or do a quick search by title here.

gan adversarial-networks arxiv neural-network unsupervised-learning adversarial-nets image-synthesis deep-learning generative-adversarial-network medical-imaging tensorflow pytorch paper cgan ct-denoising segmentation medical-image-synthesis reconstruction detection classificationThe objective is to predict continuous values, sin and cos functions in this example, based on previous observations using the LSTM architecture. This example has been updated with a new version compatible with the tensrflow-1.1.0. This new version is using a library polyaxon that provides an API to create deep learning models and experiments based on tensorflow.

lstm tensorflow recurrent-networks deep-learning sequence-prediction tensorflow-lstm-regression jupyter time-series recurrent-neural-networksThis repository contains an independent TensorFlow implementation of recurrent entity networks from Tracking the World State with Recurrent Entity Networks. This paper introduces the first method to solve all of the bAbI tasks using 10k training examples. The author's original Torch implementation is now available here. Percent error for each task, comparing those in the paper to the implementation contained in this repository.

tensorflow recurrent-neural-networks deep-learning machine-learning natural-language-processingCompared to a classical approach, using a Recurrent Neural Networks (RNN) with Long Short-Term Memory cells (LSTMs) require no or almost no feature engineering. Data can be fed directly into the neural network who acts like a black box, modeling the problem correctly. Other research on the activity recognition dataset can use a big amount of feature engineering, which is rather a signal processing approach combined with classical data science techniques. The approach here is rather very simple in terms of how much was the data preprocessed. Let's use Google's neat Deep Learning library, TensorFlow, demonstrating the usage of an LSTM, a type of Artificial Neural Network that can process sequential data / time series.

machine-learning deep-learning lstm human-activity-recognition neural-network rnn recurrent-neural-networks tensorflow activity-recognitionThis is a Tensorflow implementation of cascaded refinement networks to synthesize photographic images from semantic layouts. Required python libraries: Tensorflow (>=1.0) + Scipy + Numpy + Pillow.

image-synthesis cascaded-refinement-networks tensorflowMulti-layer Recurrent Neural Networks (LSTM, RNN) for word-level language models in Python using TensorFlow. Mostly reused code from https://github.com/sherjilozair/char-rnn-tensorflow which was inspired from Andrej Karpathy's char-rnn.

rnn tensorflow rnn-tensorflow lstmIn the paper Texture Networks: Feed-forward Synthesis of Textures and Stylized Images we describe a faster way to generate textures and stylize images. It requires learning a feedforward generator with a loss function proposed by Gatys et al.. When the model is trained, a texture sample or stylized image of any size can be generated instantly. Improved Texture Networks: Maximizing Quality and Diversity in Feed-forward Stylization and Texture Synthesis presents a better architectural design for the generator network. By switching batch_norm to Instance Norm we facilitate the learning process resulting in much better quality.

texture-networks torch neural-style style-transferSome examples require MNIST dataset for training and testing. Don't worry, this dataset will automatically be downloaded when running examples (with input_data.py). MNIST is a database of handwritten digits, for a quick description of that dataset, you can check this notebook.

recurrent-neural-networks convolutional-neural-networks deep-learning-tutorial tensorflow tensorlayer keras deep-reinforcement-learning tensorflow-tutorials deep-learning machine-learning notebook autoencoder multi-layer-perceptron reinforcement-learning tflearn neural-networks neural-network neural-machine-translation nlp cnnThis is a Tensorflow implementation of Conditional Image Generation with PixelCNN Decoders which introduces the Gated PixelCNN model based on PixelCNN architecture originally mentioned in Pixel Recurrent Neural Networks. The model can be conditioned on latent representation of labels or images to generate images accordingly. Images can also be modelled unconditionally. It can also act as a powerful decoder and can replace deconvolution (transposed convolution) in Autoencoders and GANs. A detailed summary of the paper can be found here. The gating accounts for remembering the context and model more complex interactions, like in LSTM. The network stack on the left is the Vertical stack that takes care of blind spots that occure while convolution due to the masking layer (Refer the Pixel RNN paper to know more about masking). Use of residual connection significantly improves the model performance.

deep-learning generative-algorithm paper convolution deepmind tensorflowMulti-layer Recurrent Neural Networks (LSTM, RNN) for character-level language models in Python using Tensorflow. Inspired from Andrej Karpathy's char-rnn.

Tensorflow implementation of model discussed in the following paper: Learning to Diagnose with LSTM Recurrent Neural Networks. MIMIC-III dataset can possibly be use to train and test the model. Beware this is not the data set used by the authors of the paper.

deep-learning time-series tensorflow lstm multi-label-classificationHandwriting Recognizer supports input methods that recognizes handwriting east Asian characters as used in Chinese, Japanese and Korean.

Scanner/Touchscreen Input Handwriting Recognition Software: A handwriting recognition program for Windows that is able to recognize both images from a scanner and data from a touch screen. It also works as an OCR program.

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