Classification of Stenography using Convolutional Neural Networks and Canny Edge Detection Algorithm

As technology continues to evolve, some traditional practices tend to fade. Stenography is a practice of writing dictations in terms of speed devised by Isaac Pitman. The said practice had been around the early 19th century to address the slow phased transcription process in court trials. However, such a method can diminish as people tend to rely moreover upon recently developed advanced technologies. This study aims to use Convolutional Neural Networks and image processing techniques to preserve stenography by allowing machines to recognize and use the said writing practice in modern times. This research used 2000 common court stenography words and phrases as the core dataset for training, validation, and testing. Application of canny edge detection provides the detection of edges to provide a better classification of stenographic writings. Results generated show that through Convolutional Neural Networks, stenography writings can be recognized even by the machines. Canny Edge Detection highly supported the improvement of results by allowing the model to focus more on the necessary features for better classification accuracy. The researchers also determined future enhancements and works that can contribute in the given research.