Comparative Analysis of Detection of Text from Morse Code in Handwritten Images using Convolutional Neural Networks

• Published in: 2023 4th International Conference on Electronics and Sustainable Communication Systems (ICESC) pp. 896 - 902
• Main Authors: Veeraiah, Duggineni, Basha, Shaik Johny, Raviteja, Karavadi, Prakash, Mangam Surya, Karthik, Kasturi
• Format: Conference Proceeding
• Language: English
• Published: IEEE 06.07.2023
• Subjects: AlexNet; and Convolutional Neural Network; and ResNet; Codes; Image recognition; Image segmentation; LeNet-5; Morse Code; Navigation; OCR; Predictive models; Text detection; Training
• DOI: 10.1109/ICESC57686.2023.10193691

One of the oldest techniques used in telecommunication for encoding regular characters is Morse Code. Morse Code is categorized into two separate electronic pulses which are dot (aka short pulse) and dash (aka long pulse). Detection of text from images of morse code is a complex process and there is no active research on this area As these are morse code images, different images have different styles of strokes. Our work aims to develop an Automated Morse code recognition system which is trained by a CNN (convolutional neural network) model with a self-built dataset and involves in collecting and preprocessing images of Morse code characters and creating a labeled dataset for training and testing the CNN model. The dataset creation process includes capturing images of different Morse code characters, augmenting the data to increase the dataset size, and annotating the images to label them correctly. The CNN model is then trained using the created dataset and evaluated for its accuracy in recognizing Morse code characters in images. The results demonstrate comparative analysis of different CNN based frameworks and achieved high accuracy in recognizing Morse code characters in images, making it a promising solution for automated Morse code recognition systems.