Introduction

Developing symbols that have some value is a trait unique to humans. Recognizing these symbols and understanding the letters on an image is absolutely normal for us.

On the other hand, computers need something more concrete and organized to work with. They need a digital representation, not a graphical one.

Sometimes, this simply isn’t possible. Sometimes, we wish to automate a task of rewriting text from an image with our own hands.

For these tasks, Optical Character Recognition (OCR) was devised as a way to allow computers to “read” graphical content as text, similar to how humans do. Of course, these systems, while relatively accurate, can still be off quite a bit. Even if they are, fixing up the mistakes of the system is still a lot easier and faster than doing everything from scratch by hand.

Tesseract

The technology giant, Google, has been developing an OCR engine, Tesseract, which has a decades-long history since its original inception. It offers an API for a bunch of languages, though we’ll focus on the **Tesseract **Java API.

**Tesseract **is very easy to implement and subsequently isn’t overly powerful. It’s mainly used for reading computer-generated text on black and white images, which is done with decent accuracy. Although it’s not really meant for real-world text.

For real-world, advanced Optical Character Recognition, we’d be better off using something like Google Vision, which we’ll go over in another article.

#java #tesseract #spring-boot #convert-image-to-vector