Deep Learning Approach in Gregg Shorthand Word to English-Word Conversion

Abstract

Shorthand or Stenography has been used in a variety of fields of practice, particularly by court stenographers. To record every detail of the hearing, a stenographer must write fast and accurate In the Philippines, the stenographers still used the conventional way of writing shorthand, which is by hand. Transcribing shorthand writing is time-consuming and sometimes confusing because of a lot of characters or words to be transcribed. Another problem is that only a stenographer can understand and translate shorthand writing. What if there is no stenographer available to decipher a document? A deep learning approach was used to implement and developed an automated Gregg shorthand word to English-word conversion. The Convolutional Neural Network (CNN) model used was the Inception-v3 in TensorFlow platform, an open-source algorithm used for object classification. The training datasets consist of 135 Legal Terminologies with 120 images per word with a total of 16,200 datasets. The trained model achieved a validation accuracy of 91%. For testing, 10 trials per legal terminology were executed with a total of 1,350 handwritten Gregg Shorthand words tested. The system correctly translated a total of 739 words resulting in 54.74% accuracy.