OCR, short for optical character recognition, has been around since the early days of computing. It was one of the first uses for computers. OCR is the process by which a computer reads printed text and turns it into editable data, whether typed characters or speech synthesis. It’s useful for digitizing old documents, archiving emails, and converting scanned photos into searchable text files. This article will explain how OCR works and give tips on using it with Python libraries like OpenCV, Tesseract-OCR, Image-to-text converter, and JPG-to-text converter.
What is OCR?
Optical Character Recognition (OCR) is the technology of converting images of text into editable and searchable electronic text. It’s used for a wide variety of applications, including:
- Scanning documents to keep digital copies in PDF format.
Creating searchable databases from physical documents or books - and transcribing handwritten notes into digital files for more convenient storage and later retrieval.
How does OCR Work?
For most of us, OCR is a black box. We know that it can convert images into text, but we have no idea how this happens. OCR tools use different methods to convert images into text, and the actual process varies depending on the type of OCR tool you’re using.
The most basic method of converting an image into text is called “line recognition,” which uses one or more lines as markers for finding letters and words in an image. An example would be taking an image like this:
And turning it into something like this:
There are several different types of line recognition algorithms, including optical character recognition (OCR), which uses an algorithm that looks for distinctive patterns to identify text; neural networks, which rely on artificial intelligence (AI) techniques such as machine learning; and template matching, where particular shapes within each character are used to determine what letter or word has been written down on paper or typed out digitally.
Which OCR tools are best?
There are several OCR (optical character recognition) tools that you can use to extract text from images. The most popular and easiest-to-use Image-to-text converter, JPG-to-text converter, Tesseract, is available on GitHub and in Python’s Anaconda distribution.
Commonly used packages include:
Tesseract-OCR
This package allows you to convert images into plain text. Hewlett-Packard originally developed it, but it has since been open-sourced under the Apache license. To install it on Linux or macOS, run “sudo pip uninstall Tesseract in text. It was originally developed by Hewlett-Packard but has since been open-sourced under the Apache license. To install it on Linux or macOS, runs “sudo pip install Tesseract.
Image-to-text converter
If you have a document containing images and want to search through it, OCR tools are an excellent option for making the text searchable. With this tool, all you have to do is upload your image file and select where on the page or screen of your device it’s located so that when converted into text by OCR software.
JPG to text converter
The Jpg-to-text converter is a web-based application that converts JPG images into plain text. It supports many popular image formats and does not require any download or installation process. You only need an internet connection and can convert your files in just a few seconds.
Python
Included within Anaconda Distribution for Data Science (formerly known as Continuum Analytics), this package allows users to perform optical character recognition using their images or those provided by Google Vision API services like Image Compute Engine and Cloud Vision API service.
How do I extract text from a jpg file?
- Choose any tool to get text from a photo using OCR.
- Upload or drag & drop your image.
- After receiving the result, You will receive recognized and readable text.
- You can download the file in the .docx file & copy the text on your clipboard & save it.
Advantages of using OCR tools
- Save time. OCR tools can be used to save the time of manually typing words from images, a process that would otherwise require manual labor and extensive effort by workers.
- Save money. Using OCR tools to extract text from images means you’ll spend less on hiring additional employees who could have been doing something else with their time and skill set if not for this task.
- Save effort and energy. Because you won’t have to go through the same steps as manually transcribing text from images (which takes up a lot of mental energy), using an OCR tool can help reduce the amount of stress on your mind while also saving money in terms of its physical effects on health like headaches or other pains caused by repetitive tasks such as typing documents over long periods without breaks often enough between sessions where these symptoms may become worse than before starting at work after waking up early enough so that all these tasks can be completed before going home at night. Hence, no one knows what happened at work today except maybe my boss, who might say something mean. Still, he might not because everyone else thinks he’s nice, even though they don’t know him well either way.
Conclusion
We have seen different methods of extracting texts from images using Python and how text extraction using OCR works. The article explains the different methods of extracting texts from images using Python and how text extraction using OCR works. Text can be extracted from any image with the help of OCR tools. OCR tools convert images into a digital form that humans or machines can easily read.
