AI scanning apps simplify tasks like extracting text from documents, verifying receipts, and checking data accuracy - all using your smartphone camera. With tools like Adalo, you can build a functional app without coding. Here's how:
- What It Does: These apps use OCR (Optical Character Recognition) and AI to extract and validate data from images, receipts, or documents.
- Why Adalo: Adalo's drag-and-drop builder supports mobile functionality. It integrates with OCR tools like Google Cloud Vision and allows deployment to iOS, Android, or as a PWA.
- Getting Started: Create a free Adalo account, set up an Airtable database for scanned data, and connect OCR services via APIs.
- Key Features: Add components like a camera, barcode scanner, and validation logic. Design user-friendly interfaces for scanning, reviewing, and approving data.
- Testing and Publishing: Test your app on real devices, then publish across platforms. Adalo also provides analytics to track performance.
Adalo simplifies app creation, making it faster and more accessible, even for beginners.
5 Steps to Build an AI Scanning App with Adalo
Setting Up Your Adalo Project
Creating a New Adalo App
To get started, sign up at adalo.com. Once you're in, open the App Switcher by clicking your app name in the Top Bar, then select "Create a New App." Here, you’ll have two options: choose a Feature Template or start from scratch with a blank screen. Templates are a great starting point for beginners since they come with pre-built screens, databases, and functions. If you're looking for more flexibility and have the time, starting with a blank screen gives you full control over the app's design and functionality.
After creating your app, the next step is setting up your database to efficiently handle scan data.
Designing the Database for Scans
Click on the Database icon (the spreadsheet symbol) in the left sidebar. Create a new collection and name it "Scans." Then, add the following properties using the "Add to Properties" button:
- Scan Image (Image): Stores the captured photo or document.
- Extracted Text (Text): Holds the OCR results. Leave this blank so it can be automatically filled by your AI service.
- Validation Status (Text or True/False): Tracks whether scans pass or fail checks, such as "Approved", "Rejected", or "Pending."
- Timestamp (Date): Logs when each scan occurs, using the MM/DD/YYYY format for US localization.
If needed, you can add a Relationship property to link the Scans collection to the Users collection. This is useful for tracking which user performed each scan. Taking the time to name these properties clearly will save you a lot of headaches as your app becomes more complex.
"A database is simply an organized collection of information that is accessed electronically." - Adalo
With your app and database ready, you're all set to move on to integrating scanning and AI features.
Building a Barcode Scanning App on Adalo
Building Scanning and AI Integration Features
Once your Scans database is set up, it's time to integrate scanning and AI functionality into your app.
Adding a Camera or Barcode Scanner Component
To start, click the "+" icon on your app screen to add a new component. Look for the Barcode Scanner component in the component panel or search for it in the Adalo Marketplace. This component uses the camera on mobile devices to scan various code types, such as QR codes or EAN codes.
For capturing documents or images with Optical Character Recognition (OCR), use the Image Picker component. Pair it with a button that links the captured image to the Scan Image property in your Scans collection, creating a new record.
"Best no-code tools allow us to develop and deploy your new application 40-60% faster than regular app development methods." - Bootstrapped
Integrating OCR for Data Extraction
To enable OCR functionality, connect your app to an AI-powered text recognition service using an automation tool like Zapier or Make (formerly Integromat). These platforms can act as a bridge between Adalo and OCR services like Google Cloud Vision or Nanonets OCR. Both services offer free tiers for basic usage.
In Zapier, set up a New Record trigger for your Scans collection. This workflow activates whenever a user uploads an image. Add an action step to send the image to your chosen OCR service. For Zapier, prepend https://adalo-uploads.imgix.net/ to the image file property so that the service can access it externally. Once processed, map the extracted text back to the Extracted Text property using an Update Record action.
After setting up OCR, you can move forward by adding validation rules to ensure the captured data meets your app's requirements.
Setting Up Data Validation Logic
Adalo includes built-in tools to validate data formatting. For example, dates are displayed in the MM/DD/YYYY format in your app, but external integrations may require dates in the YYYY-MM-DD format. Similarly, ensure numbers are stripped of symbols and Boolean values are formatted in lowercase.
Use Magic Text to display extracted results immediately after scanning, giving users a chance to confirm the information before proceeding. Add conditional actions to ensure records are only created or updated when all required fields are complete. Include buttons that allow users to update the Validation Status based on their review.
Once these validation rules are in place, your app will be ready to handle scanned and extracted data efficiently.
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Designing the User Interface
Crafting a user-friendly interface is key to helping users quickly scan, review, and take action on results. By building on your app's scanning and validation features, you can create an interface that feels intuitive and simplifies the user experience.
Creating Screens for Scanning and Results
Start by enhancing the scanning screen. Add tools like a Barcode Scanner or Image Picker to streamline the process. Configure the scanner so that a successful scan automatically creates a new record in the database and redirects the user to the results screen.
On the results screen, include a Simple List to display important data like product names, extracted text, and scan dates. Use Magic Text to pull this information directly from your database, eliminating the need for manual data entry and ensuring accuracy. To make navigation easier, add Search and Filter fields so users can quickly find records by date, status, or specific keywords.
"You'll need a frontend that balances beauty and simplicity - if not, users might ditch your app." - The Adalo Team
To keep the interface clean and user-focused, implement Conditional Visibility. For instance, display a green "Approved" badge when the Validation Status is "Approved" and a red "Rejected" badge when it’s "Rejected." This ensures users only see information relevant to each scanned item without unnecessary clutter.
Adding Action Buttons for Approve/Reject
On the results screen, include two action buttons: Approve (green) and Reject (red). Link these buttons to update the record's Validation Status to "Approved" or "Rejected" accordingly.
Once you've set everything up, use Adalo's Preview tool to thoroughly test the workflow. This ensures that the buttons update records correctly and that the entire process runs smoothly.
Testing and Publishing Your App
Before launching your app, it's crucial to test it thoroughly. Adalo offers tools that make transitioning from development to production seamless, whether you're targeting web or mobile platforms.
Testing on Devices and Debugging
Begin by using the Preview button in Adalo’s top bar to test your app in a web browser. This allows you to check if features like scanning workflows, validation logic, and action buttons are functioning correctly. However, browser testing has its limits, especially for AI scanning apps. It won’t give you an accurate picture of how camera integration, image capture quality, or OCR processing will perform in real-world scenarios.
For a more accurate assessment, test on physical devices. Navigate to the Launch tab and create a TestFlight build for iOS. You’ll need an Apple Developer account to proceed and must configure permissions for Camera and Photos. Apple requires clear explanations for these permissions; without them, your app submission could be rejected. Testing on actual devices, like iPhones and Android phones, helps you evaluate how your app performs under various conditions - such as different lighting, angles, and scanning speeds - all of which affect the accuracy of your AI model.
Additionally, test your app’s data validation thoroughly. Submit both valid and invalid documents to ensure your AI handles errors correctly. Use Adalo's Version History feature (indicated by a circled checkmark icon) to save up to 10 iterations of your app. This feature allows you to roll back to a previous version if any issues arise during testing.
Once you’ve confirmed your app works as intended on real devices, you’re ready to move on to publishing.
Deploying to Web, iOS, and Android
After testing, deploying your app to various platforms is straightforward. On the Editing Dashboard, click Publish to get started. For web deployment, you can use a custom domain (which typically costs $10–$45 per year) or an Adalo subdomain for an instant launch.
For iOS, enter your Apple Developer credentials and Bundle ID in the Launch tab. Be careful to avoid any extra spaces in the Bundle ID, as this could cause issues. Apple’s review process generally takes 24 to 48 hours. For Android, you’ll need a Google Play Developer account, which has a one-time fee of $25. Follow the Android-specific instructions in the Publish panel. Google’s automated review process is faster, often approving apps within a few hours.
Once your app is live, use Adalo’s Analytics tool (represented by a chart icon in the dashboard) to monitor its performance. Track metrics like total users, scan success rates, and how often users approve or reject validation results. This data can reveal how well your AI model performs in production. If users frequently reject results, it may be time to refine your AI prompts or tweak your validation logic.
Whether you compare Progressive Web Apps vs. native apps for your deployment or launch on both, Adalo allows you to make updates without rebuilding. Use the analytics data to continuously improve your app and ensure it meets user expectations.
Conclusion
Creating an AI-powered scanning and checking app using Adalo is both efficient and straightforward. In this guide, you learned how to establish a well-structured database, integrate OCR functionality, design intuitive interfaces, and launch your app across various platforms. What used to require months of development and budgets ranging from $50,000 to $250,000 can now be achieved in just a few weeks - and at a fraction of the cost.
Adalo’s drag-and-drop builder makes it easy to develop native mobile apps, incorporate AI features, and publish on web, iOS, and Android platforms. This simplified process caters to both beginners and seasoned app creators.
"No-code platforms can decrease app development time by 90%, reducing months to weeks." - The Adalo Team
If you’re new to app development, start with one of the best free app templates and test your app on actual devices to ensure the AI features work seamlessly. Whether your goal is validating receipts, verifying IDs, or streamlining inventory management, Adalo equips you with everything you need to bring your ideas to life. Ready to get started? Head over to adalo.com and turn your vision into reality.
FAQs
How do I ensure accurate text extraction when using OCR in my app?
To get precise text extraction using OCR in your app, start by designing a clear and efficient data capture process. Create a dedicated collection to store the uploaded images, the extracted text, and any related metadata for validation purposes. Include an image picker and a button to initiate the OCR process, ensuring the image is uploaded before running text extraction.
Incorporate an AI-driven OCR tool, such as Google Vision, to analyze the image and extract the text. For optimal accuracy, make sure images are sharp, well-lit, and free from blurriness. Pre-processing steps like cropping or enhancing the image can make a big difference in the results. Additionally, implement validation rules in your app - like format checks or length limits - to catch potential errors and maintain high data quality.
Finally, save the original image alongside the extracted text to allow for manual review if necessary. By combining clear image capture, robust OCR integration, and in-app validation, your app can consistently deliver accurate and dependable results.
How can I add AI-powered features to my Adalo app?
To bring AI-powered features into your Adalo app, begin by creating a data collection to store the results - this could include scanned text or validation flags. Next, integrate user-friendly tools like an image picker or barcode scanner, allowing users to easily upload or capture documents. Use Adalo’s custom actions to connect your app with AI services like OpenAI. Simply input your API key and set up the data flow between the app and the AI model. Once everything is in place, thoroughly test the app to confirm that the AI responses are accurate and the data is being stored correctly. After testing, you’re ready to publish the app for your users. The best part? This entire process doesn’t require any coding skills, making it accessible even for beginners.
How can I test my AI scanning app to ensure it works well on different devices?
To make sure your AI scanning app works smoothly across devices, it's crucial to test it extensively on both iOS and Android hardware. Begin by using the Adalo Preview app to observe how your app performs in practical scenarios. Activate developer tools to track logs and monitor network activity while testing key features like scanning, data validation, and error handling.
Experiment with scanning different file types - such as PDFs, JPGs, and PNGs - under various conditions, including low-light settings or blurred images, to see how reliably the AI processes the data. Check the app’s responsiveness on devices with varying screen sizes and orientations to ensure the layout adapts seamlessly. Additionally, simulate different network conditions, like offline mode or slow connections, to verify the app can handle these situations effectively.
To gather user feedback, include a simple in-app option for reporting issues. This feature should capture details like the device used and relevant scan data. After every update, re-test the app to identify and resolve any new problems, ensuring a consistently smooth experience for users.
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