Having texts on images has always been a challenge for accessibility and it is the rule of thumb to have few texts on an image. In short, images and videos are now searchable with the aid of OCR. With Optical Character Recognition, it is possible to recognize characters on images and use them to provide relevant image results to users. Searching algorithms are mostly based on manipulating texts.
Searching is one of the most common activities, especially on the internet. Also, hiding sensitive content on Twitter is also made possible by OCR. The regulation might be used to protect users’ rights and secure them from threats or scams.Įxtracting texts from an image enables an organization to process textual information on an image for regulation, especially when the images are supplied by some of the users.įor example, Facebook-like regulation of the number of texts on images used for ads can be achieved with OCR.
I realized the ability to extract texts from an image ensures a lot of possibilities such as:Įvery organization needs to regulate users’ activities for some reasons. So, why use OCR? Well, Optical Character Recognition solves a lot of problems, one of which triggered me to write this article. The Evolution Of OCR can be traced to several inventions but Optophone, “ Gismo”, CCD flatbed scanner, Newton MesssagePad and Tesseract are the major inventions that take character recognition to another level of usefulness. OCR stands for “Optical Character Recognition” or “Optical Character Reader”. After leaving the company, I decided to do some research and try to find possible solutions. I was willing to solve the problem but it was not necessary to solve the problem judging from the business’ or the management’s point of view. Our technical stack was good enough to work with Tesseract.js provided the issue was considered important by the management. Although we had a website through which users connected with us, the processing of gift cards was carried out manually behind the scenes.Īt the time, our website was built mainly with PHP (Laravel) for the backend and JavaScript (jQuery and Vue) for the frontend.
IMAGE OCR SCANNER MANUAL
There was a department called “Operations” within the company that was responsible for manual confirming gift cards and crediting users’ accounts. We needed to process scanned gift cards and we had to do it manually since we couldn’t extract text from images. The inability to extract text from images was a data processing limitation I experienced first-hand at my last company. These images were mostly processed manually because there was no way to process them programmatically. Sometimes, images contain essential texts that are meant to be processed to achieve a certain purpose. On the web, data is mostly collected in the form of texts, images, videos, and many more. Such information is represented as data, especially through computation.
To solve human problems, it is necessary to have some information about them.
IMAGE OCR SCANNER HOW TO
In this article, we look at how to convert an image to text with React and Tesseract.js(OCR), preprocess images, and deal with the limitations of Tesseract (OCR).ĭata is the backbone of every software application because the main purpose of an application is to solve human problems. Do you have to process data manually because it is served through images or scanned documents? An image-to-text conversion makes it possible to extract text from images to automate the processing of texts on images, videos, and scanned documents.
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