Text Extraction from Images Using Tesseract

Summary

Images play a crucial role in describing, representing, and conveying information, which are essential in many businesses and organizations. Text extraction from images is the method used to convert text to plain text. Text extraction refers to the systematic procedure used for converting textual material into a simplified plain text format. The task at hand presents a significant difficulty as a result of the many changes in variables such as the size, orientation, and alignment of the text. Furthermore, the inclusion of low‐resolution, pixelated pictures, coupled with the existence of noisy backgrounds, exacerbates the complexity associated with the process of text extraction. Using the Tesseract OCR engine, we aim to reduce these issues in this project.

Tesseract is developed by Google and is an open‐source optical character recognition (OCR) engine. OCR technology allows computers to recognize text in images, making it possible to convert images of text into machine‐readable text. Tesseract has been trained in a wide variety of languages and scripts, including English, Chinese, and Arabic.

Tesseract can process images that are rotated, tilted, or skewed and can recognize text written in different scripts, such as English and Arabic. It uses machine learning algorithms to improve its recognition accuracy over time, making it suitable for use in a wide range of applications, including document scanning, archiving, and indexing.