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.
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.
- Setting the Stage: Prerequisites
- Installing the Necessary Libraries
- Prepare a Basic Layout for Input and Output
- Upload The Test Image
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 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
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.