How to Use OCR Technology to Build an Image to Text App?

Once used to save memories, images have become more than that. These are now used to save data such as a receipt, screenshot, document, social media infographic, and more.

However, the only disadvantage of saving text/data in the image form is that it can’t be copied or edited. Users either have to manually type down the entire script or extract the text with the help of Optical Character Recognition (OCR) technology. 

OCR technology is the key to text extraction and document digitization. It enables us to convert handwritten, printed, or scanned text into editable digital formats. 

This all happens when OCR technology is put into an image to text application. Building an image-to-text app powered by OCR technology is both feasible and an excellent way to address real-world text-extraction problems in many situations. 

But how can OCR technology be used to create an image-to-text app? How much does it cost? What features can we bring into the app? Read on to learn all this!

What Is An Image to Text App?

Image to text app is a software application that anyone can use to extract text from images or scanned document copies. 

These apps use Optical Character Recognition (OCR) technology to quickly convert typed, printed, or handwritten text into digital format. Once converted, this text can be copied, edited, or shared like any other text file.

While there are many uses of image to text applications, these are especially useful for digitizing documents. These can extract data from business cards, documents, receipts, printed documents, and the list goes on.

Market Statistics and Trends for OCR Technology

OCR technology is getting popular across many industries. The use of this technology is projected to increase in the following decade. 

For your information, the global OCR market made 12,557.9 million USD in 2023. This number is expected to reach 32,902.5 million USD by 2030, which is 14.8% growth. 

The BFSI (Banking, Financial Services, and Insurance) took over 45% of the total market share in 2023. This number shows a large-scale adoption of OCR in these sectors. 

The B2B (Business-to-Business) segment dominated in the market with 75.9% of the total share against the B2C (Business-to-Customer) segment. What it means is that OCR technology is being used more in business operations. 

Considering all the aforementioned statistics and trends, we can say that the demand for OCR technology and image-to-text tools will increase in the future. 

How to Develop Image to Text App Via OCR Technology?

Developing an image to text app involves many steps (or phases in more accurate sense). Here is the summary of each step:

Know the Objective

The first step is to know the exact objective to create the app. 

Therefore, before you start working on the app development, know the app’s purpose. When you clearly understand the objective, it simplifies the designing and development part. 

The following are the primary objectives of developing an image-text application:

1. Scan and store receipts
2. Extract text from handwritten notes
3. Extract data from invoices or business cards to process further
4. Get the text from any image with format preserves

Sometimes, the purpose can be to do all the aforementioned tasks in one place. 

Mentioning again, clearly define your goals. It helps decide and take further steps accordingly. 

Plan the App’s Features

Once you’re done with the exact objective, the next step is to decide which feature you want to introduce into the app. Your object defines these features. 

Some features that every image-to-text app should have are these:

1. Easy-to-use interface
2. Accurate text extraction
3. Multiple image format support, such as JPEG, JPG, PNG, TIFF, GIF, etc. 
4. Option to copy extracted text
5. Offline functionality

These are some of the features every app should have. If you want to, you can embed even more features into the application. NOTE: Some more features of these apps are discussed in the section below. Read that to learn more about this phase. 

Select an OCR Engine

Do you know the core of an OCR-based image-to-text app? It is an OCR engine. 

An OCR Engine is the software component within an OCR system. It is responsible for analyzing the image that contains text and converting it into machine-readable text format. 

In other words, it makes computers able to read printed or handwritten characters from an image or scanned document. What it really does is identify patterns and match them to an existing character database to recognize the entire string. 

That said, there are two types of options to choose an OCR engine from: 

1. Open-source options
2. Cloud-based options

Open-source Options

Tesseract OCR is the most popular and highly accurate open-source OCR engine. 

It can be integrated into various applications for text extraction purposes. However, it is mostly accessed through a Python wrapper called ‘Pytesseract’ for ease of use. 

It’s customizable and works well with various text types. Tesseract is ideal for developers looking to save costs and have full control over the OCR process.

Cloud-Based Options

There are many famous options for cloud-based OCR engines, such as:

• Google Cloud Vision
• Amazon Textract
• ABBYY FineReader
• Adobe Acrobat
• Microsoft Azure OCR
• Docparser

All cloud-based OCR engines extract text from images and analyze documents via their cloud services. 

These engines let businesses adjust the processing power as per the demand to save on large upfront investments. These are accessible from any device with an internet connection. Cloud providers handle all the maintenance and updates. 

So, select the OCR engine you’ll use to develop the image-text app. 

Select a Programming Language

Once the OCR engine is selected, decide which programming language you’ll use. 

The following are the options you can consider. 

Python: Preferred for its simplicity and extensive libraries like OpenCV and PIL for image processing. Python integrates well with Tesseract and cloud-based OCR services.

Java or Kotlin: Commonly used for Android app development.

Swift: Ideal for building iOS apps.

Other Options: C#, C++, or JavaScript can be used depending on the app’s requirements.

All you have to do is consider your expertise (or that of your team) and the features you want to integrate into the app. NOTE: Make sure you choose the coding language that allows bringing in the desired features with ease. 

For your information, Python is the most commonly used language to design OCR applications. However, if you want to use any other, you can go for that, too. 

Develop the User Interface

Now is the time to actually start developing the app. 

The first thing you should start from is the user interface (UI) for the platform. 

Create an image input box that provides users with different options to submit the image. Allow users to upload an image from the device, copy and paste it, submit its direct URL, and/or capture it directly using the camera.

Don’t forget to include pre processing features to crop images and adjust brightness and contrast to produce accurate text extraction results. 

In the end, design a result box (a readable interface) to display the extracted text. It should also have the options to edit, copy, or export the text.

OCR Text Extraction

OCR text extraction is the core functionality of your app. All you need to do for this is connect it to the chosen OCR engine since the interface has already been developed. 

Create a functionality that passes the pre processed image to the OCR engine. It will extract text images, scanned documents, or handwritten notes and convert them into editable digital formats. 

Here is how an OCR engine generally works on the backend:

After receiving your image, the engine pre processes it. It adjusts contrast, removes noise, and aligns the photo to produce the best results.  

Here comes the most important part: text segmentation and character recognition. 

First, the image is divided into segments like lines, words, and individual characters. Then, the OCR engine uses different techniques, such as pattern matching and feature extraction, to recognize the text.  

Some OCR systems even have built-in language models, such as Artificial Intelligence (AI) and Machine Learning (ML), to predict and correct recognition errors. 

In the end, the recognized characters are compiled into a string of text.

Simply display the extracted text on the app’s interface, ready for copying, editing, or sharing.

Test and Optimize

Once your app is ready, test it to ensure it performs properly. 

Test the app on various devices and operating systems. Focus on speed and resource consumption. Ensure the app works smoothly, even on low-end devices.

Don’t forget to test with different text types, fonts, and languages to identify recognition errors. Once you find them, take the necessary measures to correct them. 

If your app passes all the tests, launch it. Make your app available on popular platforms like the Google Play Store and Apple App Store.

What next? Fix bugs, add new features, and optimize performance based on user feedback. Provide help documentation and respond to user queries promptly.

Features to Consider in the App

The best way to find which feature you should offer through your app is to research the existing image-to-text applications on the market. And note down the unique (and must-have) features of each.

Most of the reliable text extraction apps have these features:

1. Accurate text extraction
2. Multi-language support
3. Support for multiple file formats
4. Multiple options to submit the desired image, such as upload from a device, direct URL, copy-paste, and drag-drop
5. Saving history
6. Options to edit, copy, and download extract text in a file format
7. Offline functionality (optional) 
8. Easy-to-use interface
9. Free to use
10. And the list goes on.

You ask yourself which features you want to offer. Later, introduce them into your app.

An Example of Such App

Want to see an example of an image to text app? 

While many such apps are currently available, the Image to Text App by Enzipe Apps is the most simple-to-use one. It offers most of the important features we discussed earlier and lets the users extract text in 2 to 3 seconds. 

Go through all the different features of this app, learn from them, and develop your app in a similar manner. While you can offer different features, do no compromise on text extraction accuracy. 

Cost of Developing an Image to Text App

The exact cost to develop an image-to-text app depends on the complexity of the app, the features you want to include, the development team’s location, etc.

However, the estimated cost can be like this:

• Basic App: $10,000-$25,000
• Mid-Level App: $25,000- $50,000
• Advanced App with AI Integration: $50,000-$100,000.

These estimates can change based on the specific requirements of your app. For example, customizability, automated workflows, and API integrations. 

In addition, the cost can vary depending on whether you’re building the app from scratch or using existing computer vision APIs.

Revenue Models

There are many ways to monetize your image-to-text application, such as:

Ads

Display ads within the app, such as banner ads, interstitial ads, or video ads. Monetization is based on impressions (CPM), clicks (CPC), or completed actions (CPA).

However, always use non-intrusive ads to not disrupt the user experience. Offer an ad-free experience to premium users.

Premium Subscriptions

Offer additional features or remove limitations for a recurring fee, e.g., weekly, monthly, or yearly. For example, you can offer unlimited OCR scans to premium users. 

One-Time Purchase

Sell the app’s premium subscription for a one-time fee. But before that, allow users to test the app with a free trial or limited functionality before purchase. Highlight the benefits of a one-time purchase compared to recurring subscriptions.

Sell API

Provide the OCR functionality as an API to other developers and businesses. Charge based on usage, such as the number of requests or amount of data processed.

For this, create detailed documentation and SDKs for easy integration. Offer structured pricing based on API usage, with higher tiers for bulk or enterprise users.

Hybrid Revenue Models

For maximum monetization potential, consider combining the monetization models we discussed so far. Most current image-to-text apps are working on this model.

Conclusion

Developing an image-to-text app using OCR technology is a practical and innovative solution. It is one of the best ways to digitize text from various sources, such as receipts, documents, and handwritten notes.

However, when developing an image-text app, define clear objectives and provide the key features. Select the right OCR engine and programming language so you’re or your team can create efficient and user-friendly apps.

If we look at the growing demand for OCR technology and its applications across industries, it clearly highlights the potential for such apps in the future.