September 11, 2022

A Layman’s Guide to Extract Text from Images

Starting out development for data extraction of text from images can be tricky. With this article, we have consolidated beginner knowledge to help you get started.

Talking about extracting text from images, we believe being the creators of ‘VisionERA’, we can help solve the problem. A majority of data in today’s world is locked inside images. Everything is fine until the task of data collection is done manually. But since it is manual, it includes both operational cost and high TAT.

To deal with this, IT engineers and researchers came up with automation solutions. These solutions can seamlessly extract data from image making organizational document processes effective and efficient with less operational cost.

This data can help organizations create policies, change workflows, make predictions, create reports, and a lot more. 

So without further ado, let’s begin…

Ingredients of Text Extraction from Images

To begin with, the process can be divided into three parts i.e:

Acquiring the Source File: It is the image that will be used for text extraction.

Text Detection: It will help in identifying the region where the text lies.

Text Recognition: It will help extract text from the region.

Processes in Text Detection

Localization: In this process, the maximum amount of background surrounding the text is removed. This is performed on the image by either analyzing the component or region based methods. Region based methods are classified into two categories i.e. Region Growing Method, and Region Splitting & Merging Method.

Verification: Verification also often known as the classification stage can be supervised and unsupervised. Supervised algorithms used for this step knows about attributes such as color, size, texture, etc. whereas unsupervised algos simply don’t have any knowledge about it.

Processes in Text Recognition

Segmentation: The process is done to extract the bounded text from the background. Binarization and character segmentation are some of the algorithms used for this technique. Binanarization segmentation uses the k-means cluster algorithm to convert color images to grayscale. It enhances the capability of text recognition. On the other hand character segmentation is directly applied to the grayscale images. It allows for text recognition for single string, and strings that are broken or joined together for effective results.

Recognition: The last step is the recognition that converts single string characters into character of words or string. It is done using primarily two techniques: character recognition and word recognition. The sole purpose of this process is to create visual representation of words for the human user to see.

Some Examples of Techniques used for Text Extraction from Image

Below we have mentioned some common techniques used for the purpose. Some techniques are still widely used while others are used before a deep learning era:

OCR (Optical Character Recognition)

It is a widely used technique that is used by multiple systems today. Our Intelligent Document Processing platform VisionERA also takes advantage of it in collaboration with computer vision to produce effective results.

OCR has primarily been used for data entry purposes. For instance, invoices, documents related to passport, receipts, business cards, mail, etc.

There are multiple types of OCR systems i.e:

  • Optical Word Recognition
  • Intelligent Character Recognition
  • Intelligent Word Recognition

The process of OCR can be divided into multiple segments such as:

  • Pre-Processing: It involves multiple steps such as de-skew, binarization, line removal, line and word detection, etc. The purpose behind these steps is to increase the chances of text recognition from images.
  • Text-Recognition: The process involves ranking the characters that are to be extracted. It primarily involves two algorithms i.e. Matrix Matching and Feature Extraction. In matrix matching, the image is compared to an already existing glyph of characters. This matching is done on the basis of pixel by pixel comparison. With feature extraction, the image is made computationally efficient for scanning. The process focuses on multiple features of an image such as closed loops, lines, line intersections, etc.
  • Post-Processing: In this process, the extracted data is matched with an already existing dictionary. It is constrained by lexicon i.e. if the word doesn’t appear in the dictionary, there may not be a match for it. Multiple packages such as tesseract have their own dictionary for this process that give due results.

MSER (Maximum Stable Extremal Regions)

This technique takes the help of blob detection. Blob detection detects regions in an image that exhibit different properties such as color and brightness compared to its surrounding background. The technique takes the help of robust wide-line algorithm. The purpose of this algorithm is to create corresponding points within the image for text detection. 

SWT (Stroke Width Transformation)

This technique of text extraction is majorly used for text extraction from natural images as opposed to scanned documents, prints, emails, fax, etc. In this technique, a local image operator (for pixels) will check the width of a stroke. This width will be detected using pixel on pixel that corresponds to each other. By this the technique is able to figure out which portion of the image describes a particular string. To learn more about this technique, use this resource.

Top 5  Data Extraction Tools/Platform from Image

There are multiple data extraction tools in the market. Although, we decided on mentioning the ones we see fit for this list. These are:


VisionERA is an Intelligent Document Processing platform that is capable of data extraction, validation, triangulation, and storage. As the term document processing suggests, it can extract data from a variety of documents that ranges from structured to unstructured. The platform is backed by multiple AI technologies making it a one-stop solution for document processing for SMEs and large enterprises. VisionERA offers custom DIY workflow and it is an industry & use cases agnostic solution i.e. it has the capability to adapt to any use case and automate the entire document processing workflow.


A great tool that is used as a web scraper. It doesn’t require any coding and provides multiple great features such as point & click interface, capability to scrape any website, cloud extraction, etc. The company was established in March, 2016, and has been serving its clientele since then.


Another great tool that is capable of identifying and extracting data from multiple file formats such as Word, PDF, Image-based documents, etc. It takes the help of OCR technology, pattern recognition, and anchor keywords to complete its process. It can handle a variety of business documents, finance & accounting, and multiple other templates.

Mail Parser

A majority of our data is stuck within emails. Mail parser is a tool that helps you extract that data with ease. It has the capacity to capture data from incoming emails and provide it in usable formats such as Excel, XML, JSON, etc.



Mozenda works in the domain of providing data to companies. They do it by data harvesting from the web, delivering data projects, and taking outsourced work of data extraction. They offer their solution in two ways: in-house and cloud based. They have multiple packages for different levels of data accumulation i.e. standard, corporate, and enterprise.

Final Words

Data extraction from images is a commonly recurring process. Albeit that multiple developers are trying to come up with their own solution to help solve the problem. With this article, we have aimed to provide you a basic understanding of how data extraction from images can be done. In our pursuit, we hope that this article may have provided you with certain insights that might be useful for you.

In case, if you are looking for a full-fledged document processing solution, you can book a demo for “VisionERA” by using our “Schedule a demo button” or Clicking here.

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