Convolutional Recurrent Neural Networks(CRNN) for Scene Text Recognition
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Updated
May 9, 2023 - Python
Convolutional Recurrent Neural Networks(CRNN) for Scene Text Recognition
Pytorch Implementation For LPRNet, A High Performance And Lightweight License Plate Recognition Framework.
Connectionist Temporal Classification (CTC) decoding algorithms: best path, beam search, lexicon search, prefix search, and token passing. Implemented in Python.
Use Convolutional Recurrent Neural Network to recognize the Handwritten line text image without pre segmentation into words or characters. Use CTC loss Function to train.
Recurrent Neural Network and Long Short Term Memory (LSTM) with Connectionist Temporal Classification implemented in Theano. Includes a Toy training example.
Lightweight CRNN for OCR (including handwritten text) with depthwise separable convolutions and spatial transformer module [keras+tf]
Application of Connectionist Temporal Classification (CTC) for Speech Recognition (Tensorflow 1.0 but compatible with 2.0).
A fully convolution-network for speech-to-text, built on pytorch.
VietASR - Vietnamese Automatic Speech Recognition
Speech Recognition model based off of FAIR research paper built using Pytorch.
This repo contains code written by MXNet for ocr tasks, which uses an cnn-lstm-ctc architecture to do text recognition.
An implementation of RNN-Transducer loss in TF-2.0.
A TensorFlow implementation of hybird CNN-LSTM model with CTC loss for OCR problem
RNN CTC by using TensorFlow.
The Learnable Typewriter: A Generative Approach to Text Line Analysis
Pytorch implementation of HTR on IAM dataset (word or line level + CTC loss)
Automatic Speech Recognition
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