A major drawback of the existing system for identifying the handwriting and differences are the image size, type and content. To mitigate that issue, the group decided that the input image of the algorithm will be created by the user … Continue reading →

After the handwriting has been predicted through the use of comparison to the MNist supplied data, the handwriting image will be compared to the image of the predicted number in a certain font. For example, if the number predicted is … Continue reading →

Tensorflow will be implemented in the code as the main library to read and convert the text images into text. The code provided by Niek Temme will be the basis of our handwriting to text algorithm. The code uses the … Continue reading →

From our understanding, the code uses three neural networks to recognize handwriting, Convulational neural networks (CNN), recurrent neural networks (RNN) and a Connectionist Temporal Classification. The neural networks identify the letters of words by characters; hence it will identify other … Continue reading →

While trying to run the code, the group ran with various problems regarding tensorflow. The first wall we hit was that we had difficulties installing the library with our current version of Python. Instead of using a python system environment, … Continue reading →

The group decided to use Python 3 as the coding language for this project as it is a language, we are most familiar with and also has libraries supporting artificial intelligence coding. Since we are not familiar with handwriting to … Continue reading →