Handwritten text recognition system using Raspberry Pi with OpenCV TensorFlow

Jamil Alsayaydeh, Jamil Abedalrahim and Tommy, Lee Chuin Jie and Bacarra, Rex and Ogunshola, Benny and Mohd Yaacob, Noorayisahbe (2025) Handwritten text recognition system using Raspberry Pi with OpenCV TensorFlow. International Journal Of Electrical And Computer Engineering (IJECE), 15 (2). pp. 2291-2303. ISSN 2088-8708

Text 0248717082025142211.pdf Available under License Creative Commons Attribution Share Alike. Download (732kB)

Abstract

Handwritten text recognition (HTR) technology has brought about a revolution in the way handwritten data is converted and analyzed. This proposed work focuses on developing a HTR system using deep learning through advanced deep learning architecture and techniques. The aim is to create a model for real-time analysis and detection of handwritten texts. The proposed deep learning architecture that is convolutional neural networks (CNNs), is investigated and implemented with tools like OpenCV and TensorFlow. The model is trained on large handwritten datasets to enhance recognition accuracy. The system’s performance is evaluated based on accuracy, precision, real-time capabilities, and potential for deployment on platforms like Raspberry Pi. The actual outcome is a robust HTR system that can convert handwritten text to digital formats accurately. The developed system has achieved a high accuracy rate of 91.58% in recognizing English alphabets and digits and outperformed other models with 81.77% mAP, 78.85% precision, 79.32% recall, 79.46% F1-Score, and 82.4% receiver operating characteristic (ROC). This research contributes to the advancement of HTR technology by enhancing its precision and utility.

Item Type:	Article
Uncontrolled Keywords:	Convolutional neural networks Deep learning, Handwritten text recognition, Real-time analysis, Recognition accuracy
Divisions:	Faculty Of Electronics And Computer Technology And Engineering
Depositing User:	Norfaradilla Idayu Ab. Ghafar
Date Deposited:	13 Apr 2026 08:02
Last Modified:	13 Apr 2026 08:02
URI:	http://eprints.utem.edu.my/id/eprint/29646
Statistic Details:	View Download Statistic

Actions (login required)

View Item