Skip to content
Simple neural network library and web-based recognizer of hand drawn Unicode symbols.
C# JavaScript PowerShell HTML Pascal CSS
Branch: master
Clone or download

Latest commit

Fetching latest commit…
Cannot retrieve the latest commit at this time.


Type Name Latest commit message Commit time
Failed to load latest commit information.


Marek's Unicode Symbol Recognizer

Mausr is a neural network library written from scratch and used for recognition of hand written unicode symbols. The library is relatively general and written with emphasis on datastructures, and extensibility (and maybe a little of performacne, too :).

The main reason behind this project was my personal interest in neural networks. I decided to create unicode symbol recognizer because I often find myslef googling for an unicode symbol and it takes too much time.

Author: Marek Fiser < >

Running instance:

License: The MIT license, see LICENSE.txt for details.

Main features of neural net library

  • Basic neural network layout with input layer, output layer, and any number of hidden layers.
  • Extensible neuron activation function, stadnard sigmoid function implemented.
  • Extensible net cost function, standard logistic regression cost function implemented.
  • Extensible gradient based optimization algorithms with visual and algorithmical tests, implemented four:
    • Basic gradient descent,
    • Gradient descent with momentum, and
    • RProp+ algorithm.
    • iRProp- algorithm.
  • Back-propagation learning algorithm.
    • Efficient, vectorized, and paralellized implementation.
    • Regularization implemented to avoid overfitting.
  • Contains around 25 unit tests that ensure correctness of core components of training and evaluation algorithms.
    • Also contains simple visual tests of optimization algorithms to ensure expected behavior.

Main features of web interface

  • Search for hand drawn symbols using canvas.
  • Interface for neural network settings and training.
    • Includes real-time visual feedback of traind and test validation erors using signal-r and google chart API.
  • Interface for training of new symbol drawings.
  • Interface for approving anonymously submitted trainng data.
  • Database for storing symbols, drawings, and users.
You can’t perform that action at this time.