Streamlining Data Entry with OCR Technology
Imagine working in a bank where you need to input customer data from a form into a computer. The process can be tedious and prone to human errors. But what if you could simplify this task with Optical Character Recognition (OCR) technology? This innovative solution uses algorithms to extract text from images with high accuracy, making data entry faster and more efficient.
The Problem with Manual Data Entry
Manual data entry can be a time-consuming and laborious process, especially when dealing with a large volume of forms. The risk of human error is high, and verifying the accuracy of the entered data can be a challenge. However, with OCR technology, you can take a picture of the user’s form and let the computer fill in the data for you, reducing the likelihood of errors and increasing productivity.
Building a Text Detector in React Native
In this article, we will explore how to build a text detector in React Native using Google’s Vision API. This powerful tool enables you to extract text from images with remarkable accuracy. We’ll guide you through the process of creating a project, installing the necessary modules, coding utility functions, building an image picker component, connecting Google Cloud Vision, and rendering OCR data to the UI.
Getting Started with Google Cloud Vision
To begin, navigate to the Google Cloud Console and create a new project. Next, enable the Cloud Vision API and create an API key for authorization purposes. This key will be used to incorporate Google’s Cloud Vision API into your app.
Building the Project
Create a new project using Expo CLI and select the minimal template. Install the expo-image-picker module, which allows clients to pick photos from their camera roll. Then, create a helperFunctions.js file containing utility functions that will be used throughout the project.
Coding Utility Functions
In the helperFunctions.js file, define the API_KEY constant and create a generateBody function to generate a payload for the request. This function accepts an image parameter containing base64-encoded data. Append the code to send a POST request to Google’s API and output the server’s response to the console.
Building the Image Picker Component
Create an ImagePickerComponent.js file, which will allow users to select a photo from their gallery. Write the code to prompt the user to select a file, and upon submission, pass the image’s base64 data to the onSubmit handler.
Connecting Google Cloud Vision
Edit the App.js file to replace the onSubmit handler with callGoogleVisionAsync, which sends the user’s input to Google servers for OCR operations. This will successfully procure text from the image.
Rendering OCR Data to the UI
Finally, display the extracted text to the UI by editing the ImagePickerComponent.js file. Set the value of the text Hook to the response data and display the value of the text variable.
The Power of OCR Technology
Our brand new OCR app can be used in various situations, such as retail and marketing, where supermarkets use text detection technology to scan and store discount coupons. Government institutions also use OCR to digitize records, ensuring that user data is validated and follows a given format.
Start Exploring OCR Technology Today
If you encountered any difficulties, feel free to play with and deconstruct the code to fully understand its inner workings. Thank you for reading, and happy coding!