Applicability of OCR Engines for Text Recognition in Vehicle Number Plates, Receipts and Handwriting.

Optical character recognition (OCR) is a computer vision technique that enables computers to recognize text from images. Text detection and computer vision have made significant advancements, leading to the development of various OCR technologies. However, selecting the most suitable OCR system for a specific purpose has become a challenging task. This research paper aims to explain the theoretical concepts and mathematical formulas underlying OCR engines, providing a better understanding of their functioning and performance. The analysis covers various aspects, including the theories, algorithms, and techniques employed by OCR engines. This paper presents experiments conducted on five different image categories: vehicle number plates, receipts, handwriting, symbols and plain text images. Evaluation metrics such as Character Error Rate (CER), Word Error Rate (WER), Insertion Error Rate (IER), Deletion Error Rate (DER), End-to-end recognition rate (EEER), Word Error Rate (WER), Recall, Precision, and F1-score were utilized. The findings reveal that OCR systems perform well on plain documents, with recall and F1-score values exceeding 0.85 and 0.8, respectively. However, there are still areas for improvement in OCR, which are discussed in detail in this paper. This research provides valuable insights for researchers, developers, and practitioners interested in employing OCR technology for their commercial projects. [ABSTRACT FROM AUTHOR]