Detection and Comparative Analysis of Handwritten Words of Amharic Language to English using CNN-Based Frameworks

• Main Authors: Basha, Shaik Johny, Veeraiah, Duggineni, Charan, Boddu Venkat, Joyce Yeddu, Wiltrud Sahithi, Babu, Devalla Ganesh
• Format: Conference Proceeding
• Language: English
• Subjects: AlexNet; Amharic Character Recognition; Amharic Language; and ResNet-152; Character recognition; Computational modeling; Convolution; Lenet-5; Neural networks; Optical Character Recognition; Symbols; Training; Writing
• ISSN: 2767-7788
• DOI: 10.1109/ICICT57646.2023.10134103

Amharic is made the official working language of Ethiopia from the late 12th century onwards. Amharic is a language with a total of 57.5 million speakers, out of which 32.4 million people are being the first-language speakers or native speakers and remaining are alternative-language speakers. Amharic is a distinctive syllabic writing system that employs 33 consonant symbols and each one's seven vowel variants. Due to the ambiguity among constant symbols, it is difficult to convert handwritten simple words in Amharic to English. This research study focuses on detecting the text in Amharic words in handwritten images and converting them to English text using various Convolution Neural Network Based frameworks such as AlexNet, Lenet-5, and ResNet-152 available today. For that, a new dataset has been created with more than 60,000 training and 10000 testing images of Amharic constants written by various age groups and named it LBRAMHARIC. After that, various CNN-Based frameworks are trained with LBRAMHARIC and tested on the random inputs and customized input images. In the end, a comparison has been made on the obtained results by considering the different evaluation criteria such as accuracy and prediction rate.