Images having text in them are like locked treasure chests. You can extract the gems without a key and the key to extract text from images is OCR. Optical Character Recognition aka OCR, the magic spell, is the technology that enables computers and programs to recognize patterns in a given image.
Recognized patterns are then matched with the character sets in the database. And finally composed text is presented to you extracted from the image.
In this guide, I’ll guide you through the steps of extracting text from the images using OCR with JavaScript.
I’ll also be unveiling the importance of OCR and its applications in the real world. This article is going to be a bit technical. And I hope you have basic concepts of programming because this article might not be fully understandable to a person with zero technical knowledge.
Why Extract Text from Images?
Though I tried to give a brief answer to this question in the introduction paragraph, if the question is still pining in your mind, let me answer that first.
We are always surrounded by the visual content. When you’re in the classroom, you have books, notes, and papers. When you’re out on the road, you see billboards and other such signs. When you’re online, you see images with quotes, etc.
Handwritten sticky notes, invoices, and official documents, the list of visual content is endless.
Information in such immense visual content is sometimes required. For example, while maintaining accounting records, you need information on the invoices and receipts. Here manually extracting text from images can be time-consuming and hectic. OCR comes to help in such situations.
There are Opensource JavaScript libraries that empower OCR to extract text from the images. How JavaScript does the magic, I’ll be explaining that in the coming paragraphs.
- Setting the Stage: Prerequisites
Before we start our journey of creating image to text converter with powerful JavaScript libraries, you need to get a code editor. There are plenty of Online and Desktop based code editors. I personally use Sublime Text as it’s very lightweight and free. You can choose any tool of your choice.
- Installing the Necessary Libraries
Once you have the code editor, select the JavaScript environment so that it may auto-load relevant syntax and stuff. Now you need to import JavaScript libraries that can reduce your coding workload and you can extract text from images with your own OCR.
There are many libraries, two most popular open-source JavaScript libraries are TessseractJS and Orcad.JS. You can install any of the libraries via NPM or CDN (Content Delivery Network).
- Prepare a Basic Layout for Input and Output
Libraries, once loaded, are ready to perform the magic. Now you need to prepare a basic layout to upload the image so that JavaScript can extract text from it. This is the step for which I told earlier that this guide is for people with basic programming concepts.
You can use HTML to draft the basic layout. Then you can define the input function in JavaScript and embed it in HTML. This function will serve as a bridge between User and JavaScript Libraries. You can beautify your input form with CSS if you want to.
- Upload The Test Image
You are almost ready to test and trial your OCR. But it’s still in the baby cot. Upload the first time to test the working. Based on the output, you will have to set the parameters, settings, and options in the JavaScript.
You can choose the character sets that OCR will be matching. You can define language here. In short, it’s like fine-turning of OCR to make it mature.
- Handling Extracted Data
This is a crucial step. JavaScript libraries will help you extract characters from the given image. Now you have to tell your OCR how to arrange and organize the extracted data. You will have to define rules here in your code.
This is crucial because it defines the accuracy of your OCR. If an OCR extracts raw characters with no meaningful information in them, it will be useless.
- Post Processing
It is the continuity of the previous step. Here the algorithm will be defined to tell the OCR what to keep and what to ignore. This step is also crucial because in this step your OCR will be able to recognize and handle special characters.
Once this step is done, your OCR is almost ready to perform the magic. But it’s still not commercial-level accurate or mature. To make it mature, you will have to keep testing it with different images and repeat the process of fine-tuning.
- Power It Up by AI
JavaScript libraries will perform the basic text extraction from the images. But fine-tuning can be powered up by adding AI to your OCR. AI will enhance accuracy to the optimum level.
You can use NLP and ML to ensure that the information extracted is readable and meaningful even when the uploaded image isn’t of good quality.
Summing Up
So, hope this step-by-step guide has given you a good idea about the role of JavaScript in extracting text from images.
If you have technical backgrounds and know a little about coding, you can follow this guide to create your own OCR using open-source JavaScript libraries. You can ask for further information in the comment section.