Scene Text detection and Recognition by Using Multi-Level Features Extractions Based on You Only Once Version Five (YOLOv5) and Maximally Stable Extremal Regions (MSERs) with Optical Character Recognition (OCR)

Abstract

 Textual information within scene images is very important in computer vision applications such as
image retrieval based on its content, Tourist translator, and Navigation systems assistant. This paper presented a
scene text recognition system based on YOLO. The main steps of the suggested methodology are: text detection
and localization (using YOLOv5), text segmentation (using Morphological processing as a new method), features
extraction (using MSERs), character segmentation and word segmentation (using bounding boxes with graph), and
finally character recognition (using OCR). In this work we create a new dataset model that includes most of the
text challenges such as Font type, Font size, Font color, and Font. The proposed system gives higher performance
for detection, localization, and recognition when using dataset containing many challenges, the results were 80%,
96%, and 87.6 for precision, recall, and F-score respectively. Comparing with other similar works it was better.
The accuracy of (OCR) is more than 99%.