Embedded System Based Raspberry Pi 4 for Text Detection and Recognition

Abstract

Detecting and recognizing text from natural scene images presents a challenge because the image quality depends on the conditions in which the image is captured, such as viewing angles, blurring, sensor noise, etc. However, in this paper, a prototype for text detection and recognition from natural scene images is proposed. This prototype is based on the Raspberry Pi 4 and the Universal Serial Bus (USB) camera and embedded our text detection and recognition model, which was developed using the Python language. Our model is based on the deep learning text detector model through the Efficient and Accurate Scene Text Detector (EAST) model for text localization and detection and the Tesseract-OCR, which is used as an Optical Character Recognition (OCR) engine for text recognition. Our prototype is controlled by the Virtual Network Computing (VNC) tool through a computer via a wireless connection. The experiment results show that the recognition rate for the captured image through the camera by our prototype can reach 99.75% with low computational complexity. Furthermore, our prototype is more performant than the Tesseract software in terms of the recognition rate. Besides, it provides the same performance in terms of the recognition rate with a huge decrease in the execution time by an average of 89% compared to the EasyOCR software on the Raspberry Pi 4 board.