990 Open source projects that are alternatives of or similar to Lstm Ctc Ocr

Recurrent Neural Network and Long Short Term Memory (LSTM) with Connectionist Temporal Classification implemented in Theano. Includes a Toy training example.

ICDAR 2019 Robust Reading Challenge on Scanned Receipts OCR and Information Extraction

Some of the ML and DL related reading materials, research papers that I've read

CNN+LSTM+CTC based OCR implemented using tensorflow.

Tensorflow-based CNN+LSTM trained with CTC-loss for OCR

主流ocr算法研究实验性的项目，目前实现了CNN+BLSTM+CTC架构

This project seeks to utilize Deep Learning models, Long-Short Term Memory (LSTM) Neural Network algorithm, to predict stock prices.

Keras implementation of Legendre Memory Units

Tutorials on getting started with PyTorch and TorchText for sentiment analysis.

Sequence analyzing using Recurrent Neural Networks (RNN) based on Keras

Convolutional Recurrent Neural Network(CRNN) for End-to-End Text Recognition - TensorFlow 2

Распознавание речи русского языка используя Tensorflow, обучаясь на базе Voxforge

TensorFlow 1.3 experiment with LSTM (and GRU) RNNs for sine prediction

Ready-to-use OCR with 80+ supported languages and all popular writing scripts including Latin, Chinese, Arabic, Devanagari, Cyrillic and etc.

A bidirectional LSTM with attention for multiclass/multilabel text classification.

Anomaly detection for temporal data using LSTMs

Tensorflow Implementation of Recurrent Neural Network (Vanilla, LSTM, GRU) for Text Classification

Handwritten Text Recognition (HTR) system implemented with TensorFlow.

Malicious URL detector using keras recurrent networks and scikit-learn classifiers

PyTorch/Tensorflow solutions for Stanford's CS231n: "CNNs for Visual Recognition"

RNN benchmarks of pytorch, tensorflow and theano

RNNSharp is a toolkit of deep recurrent neural network which is widely used for many different kinds of tasks, such as sequence labeling, sequence-to-sequence and so on. It's written by C# language and based on .NET framework 4.6 or above versions. RNNSharp supports many different types of networks, such as forward and bi-directional network, sequence-to-sequence network, and different types of layers, such as LSTM, Softmax, sampled Softmax and others.

OCR engine for all the languages

A research project for text detection and recognition using PyTorch 1.2.

Application of machine learning to the Coinbase (GDAX) orderbook

Anomaly detection implemented in Keras

Connectionist Temporal Classification (CTC) decoder with dictionary and language model for TensorFlow.

Bitcoin price Prediction ( Time Series ) using LSTM Recurrent neural network

Plain Stock Close-Price Prediction via Graves LSTM RNNs

A neural network to generate captions for an image using CNN and RNN with BEAM Search.

[DEPRECATED] A Neural Network based generative model for captioning images using Tensorflow

A recurrent attention module consisting of an LSTM cell which can query its own past cell states by the means of windowed multi-head attention. The formulas are derived from the BN-LSTM and the Transformer Network. The LARNN cell with attention can be easily used inside a loop on the cell state, just like any other RNN. (LARNN)

pytorch-kaldi is a project for developing state-of-the-art DNN/RNN hybrid speech recognition systems. The DNN part is managed by pytorch, while feature extraction, label computation, and decoding are performed with the kaldi toolkit.

News summarization using sequence to sequence model with attention in TensorFlow.

Deep-learning model presented in "DataStories at SemEval-2017 Task 6: Siamese LSTM with Attention for Humorous Text Comparison".

Using LSTM network for time series forecasting

Speaker Diarization is the problem of separating speakers in an audio. There could be any number of speakers and final result should state when speaker starts and ends. In this project, we analyze given audio file with 2 channels and 2 speakers (on separate channels).

A new fork base on tess-two and Tesseract 4.0.0

Image Captioning: Implementing the Neural Image Caption Generator with python

[AAAI 2020] Modeling Personality with Attentive Networks and Contextual Embeddings

A Python package for gender classification.

This package contains an OCR engine - libtesseract and a command line program - tesseract. Tesseract 4 adds a new neural net (LSTM) based OCR engine which is focused on line recognition, but also still supports the legacy Tesseract OCR engine of Tesseract 3 which works by recognizing character patterns. Compatibility with Tesseract 3 is enabled by using the Legacy OCR Engine mode (--oem 0). It also needs traineddata files which support the legacy engine, for example those from the tessdata repository.

Human Activity Recognition example using TensorFlow on smartphone sensors dataset and an LSTM RNN. Classifying the type of movement amongst six activity categories - Guillaume Chevalier

🥕 Evolutionary Neural Networks in JavaScript

CRNN with attention to do OCR,add Chinese recognition

Tensorflow implementation of Synthetic Gradient for RNN (LSTM)

The Official Deep Learning Framework for Route-based Place Recognition

BasicOCR是一个致力于解决自然场景文字识别算法研究的项目。该项目由长城数字大数据应用技术研究院佟派AI团队发起和维护。

Lightweight C++11 library for building deep recurrent neural networks

List of papers, code and experiments using deep learning for time series forecasting

Named Entity Recognition using multilayered bidirectional LSTM

Connectionist Temporal Classification (CTC) decoding algorithms: best path, prefix search, beam search and token passing. Implemented in Python.

Visualization Toolbox for Long Short Term Memory networks (LSTMs)

Named Entity Recognition with RNN, implemented by Chainer

Sentiment Analysis on the First Republic Party debate in 2016 based on Python,NLTK and ML.

A tutorial on how to implement models for part-of-speech tagging using PyTorch and TorchText.

PyTorch tutorial for learners

A Keras library for multi-step time-series forecasting.

Sequence prediction using recurrent neural networks(LSTM) with TensorFlow

TensorFlow implementation of multi-layer recurrent neural networks for training and sampling from texts