|Project Name||Stars||Downloads||Repos Using This||Packages Using This||Most Recent Commit||Total Releases||Latest Release||Open Issues||License||Language|
|Openface||14,540||5 months ago||13||apache-2.0||Lua|
|Face recognition with deep neural networks.|
|The Incredible Pytorch||9,479||20 days ago||1||mit|
|The Incredible PyTorch: a curated list of tutorials, papers, projects, communities and more relating to PyTorch.|
|Trape||7,420||8 days ago||232||Python|
|People tracker on the Internet: OSINT analysis and research tool by Jose Pino|
|Deep Learning Roadmap||3,139||3 months ago||2||mit||Python|
|:satellite: Organized Resources for Deep Learning Researchers and Developers|
|Deep Learning Papers Translation||2,807||3 years ago||1|
|Awesome Speech Recognition Speech Synthesis Papers||2,488||19 days ago||1||mit|
|Automatic Speech Recognition (ASR), Speaker Verification, Speech Synthesis, Text-to-Speech (TTS), Language Modelling, Singing Voice Synthesis (SVS), Voice Conversion (VC)|
|Sparseconvnet||1,781||2 months ago||2||July 10, 2019||50||other||C++|
|Submanifold sparse convolutional networks|
|Deep Anpr||1,702||3 years ago||98||mit||Python|
|Using neural networks to build an automatic number plate recognition system|
|Awesome Scene Text Recognition||1,561||5 years ago||4|
|A curated list of resources dedicated to scene text localization and recognition|
|Image Text Localization Recognition||883||8 months ago|
|A general list of resources to image text localization and recognition 场景文本位置感知与识别的论文资源与实现合集 シーンテキストの位置認識と識別のための論文リソースの要約|
This program illustrates the pattern recognition capabilities of the multi-layer perceptron with an optical character recognition application. The program has a learning database which contains the matrix representations of the digits from 0-9. After learning the user can draw digits in the application and the program will try to recognize that digit.
In this panel we can set the number of the neurons on the hidden layers. For example in this case we set the network with one hidden layer which contains 10 neurons.
We can load samples to the network by pressing the load button. After loading samples we can set the parameters like momentum, learning rate and the number of iterations. The learn button will starts the network to learn and as a result we can see the error on the graph, in the function of iterations. We can press the learn button more times and we can see the results of the learning iterations on the graph.
To test the knowledge of the network first we need to draw a number in the square with the mouse. After we draw a number we should press the Test button and we will see the results of the recognition. As a result we can see a percent estimation to each character, and the number with the highest probability is marked with red color.