#TEXT EXTRACTOR API CAMERA SERIES#

The image is now split into zones identifying the areas of interest such as where the images or text are and this helps kickoff the extraction process.

The image is then pre-processed where the brightness and contrast are adjusted to enhance the accuracy of the process. The image is first scanned and the text and graphics elements are converted into a bitmap, which is essentially a matrix of black and white dots.

#TEXT EXTRACTOR API CAMERA SERIES#

How is it really achieved? To us, text on an image is easily discernible and we are able to detect characters and read the text, but to a computer, it is all a series of dots. Upon identification, the character is converted to machine-encoded text. An image containing text is scanned and analyzed in order to identify the characters in it. Optical Character Recognition involves the detection of text content on images and translation of the images to encoded text that the computer can easily understand. We will also build a simple script in Python that will help us detect characters from images and expose this through a Flask application for a more convenient interaction medium. In this article, we will delve into the depth of Optical Character Recognition and its application areas. While it's not always perfect, it's very convenient and makes it a lot easier and faster for some people to do their jobs.

Whether it's recognition of car plates from a camera, or hand-written documents that should be converted into a digital copy, this technique is very useful. This is where Optical Character Recognition (OCR) kicks in. They need something more concrete, organized in a way they can understand.

We perceive the text on the image as text and can read it.Д¬omputers don't work the same way. Humans can understand the contents of an image simply by looking.

Text extractor api camera