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    Text Recognition

    Rumman Ansari June 08, 2026 15 views Subject Details

    Text Recognition in AI Builder

    Text Recognition is an important feature of Microsoft AI Builder that allows users to extract text from images and documents. It uses Optical Character Recognition, commonly known as OCR, to identify printed or handwritten text and convert it into machine-readable text.

    In simple words, Text Recognition helps a computer read text from files such as scanned documents, photos, PDFs, receipts, labels, forms, and handwritten notes. This extracted text can then be used in Power Apps, Power Automate, Dataverse, SharePoint, Excel, or other business systems.

    What is Text Recognition?

    Text Recognition is the process of detecting and extracting text from images or document files. It is mainly based on OCR technology. OCR stands for Optical Character Recognition.

    OCR helps convert text present inside an image into editable and searchable digital text. For example, if a scanned document contains the text "Invoice Number: INV-1001", Text Recognition can read that text and return it as digital output.

    Text Recognition in AI Builder

    In AI Builder, Text Recognition is available as a prebuilt AI model. A prebuilt model means the user does not need to train the model from the beginning. Microsoft has already trained the model to recognize text from images and documents.

    Users can directly use the Text Recognition model in Power Apps and Power Automate to extract text from uploaded files, scanned documents, photos, or PDF files.

    Why Text Recognition is Important?

    Importance Explanation
    Reduces manual typing Users do not need to manually type text from scanned documents or images.
    Saves time Large numbers of documents can be processed faster using automation.
    Improves accuracy It helps reduce human errors during data entry.
    Supports automation Extracted text can be used in Power Automate workflows for further processing.
    Makes documents searchable Text from images or scanned files can be converted into searchable digital content.

    How Text Recognition Works

    Text Recognition works by analyzing the content of an image or document file. The AI model scans the file, identifies text areas, reads the characters, and returns the recognized text as output.

    1. The user uploads or provides an image, scanned document, or PDF file.
    2. AI Builder analyzes the file using OCR technology.
    3. The model detects printed or handwritten text.
    4. The detected text is converted into machine-readable digital text.
    5. The extracted text can be used in apps, flows, databases, or reports.

    Examples of Text Recognition

    Input File Text Recognition Output
    Scanned invoice Invoice number, date, customer name, and amount can be extracted.
    Receipt image Store name, purchase items, total amount, and date can be extracted.
    Photo of a notice board Important announcement text can be extracted.
    Handwritten note Handwritten content can be converted into digital text.
    PDF document Text from the PDF can be extracted and used in workflows.

    Key Terms Used in Text Recognition

    Term Meaning
    OCR Optical Character Recognition; technology used to read text from images and documents.
    Prebuilt Model A ready-made AI model that can be used without custom training.
    Printed Text Text printed in books, forms, invoices, receipts, or documents.
    Handwritten Text Text written manually by a person using pen, pencil, or stylus.
    Machine-readable Text Digital text that can be copied, searched, stored, or processed by software.
    Document Processing The process of extracting useful information from documents.

    Where Text Recognition Can Be Used

    • Invoice text extraction
    • Receipt processing
    • Reading scanned application forms
    • Extracting text from ID cards
    • Reading handwritten notes
    • Extracting text from PDFs
    • Reading text from photos
    • Processing delivery challans and purchase orders
    • Digitizing paper-based business documents

    Text Recognition with Power Apps

    Text Recognition can be used in Power Apps to build intelligent applications where users can upload or capture images and extract text from them.

    Example:

    • A user opens a Power App on a mobile device.
    • The user captures a photo of a receipt.
    • The AI Builder Text Recognition model reads the text from the receipt.
    • The app displays the extracted text on the screen.
    • The user can save the extracted data into Dataverse or SharePoint.

    Text Recognition with Power Automate

    Text Recognition can also be used in Power Automate to automate document-based workflows. For example, when a PDF or image is uploaded to a SharePoint folder, a flow can automatically extract text from the file and save the output to another system.

    Example automation flow:

    1. A file is uploaded to a SharePoint document library.
    2. Power Automate starts the flow automatically.
    3. The file is sent to the AI Builder Text Recognition model.
    4. The model extracts the text from the file.
    5. The extracted text is stored in Dataverse, Excel, or a SharePoint list.
    6. A notification can be sent to the user or team.

    Difference Between Text Recognition and Form Processing

    Point Text Recognition Form Processing Model
    Purpose Extracts general text from images and documents. Extracts specific fields from structured or semi-structured forms.
    Training Required No custom training is usually required because it is a prebuilt model. Training is required using sample documents.
    Output Returns detected text lines or full text. Returns specific values such as invoice number, date, and amount.
    Best For Reading text from images, PDFs, and scanned files. Extracting business fields from forms and invoices.
    Example Extract all text from a scanned notice. Extract invoice total and vendor name from invoices.

    Real-Life Business Use Cases

    Business Area Use Case Example
    Finance Receipt and invoice reading Extract text from receipts and invoices for expense processing.
    Human Resources Document digitization Extract text from employee forms and onboarding documents.
    Logistics Delivery document processing Read delivery challans, shipment labels, and package documents.
    Education Note digitization Convert handwritten or scanned notes into digital text.
    Customer Service Complaint form processing Extract text from scanned customer complaint forms.

    Example Scenario: Receipt Text Extraction

    Suppose an organization wants to automate employee expense claims. Employees submit receipt images through a Power App. Instead of manually typing the details, AI Builder Text Recognition can read the text from the receipt.

    The solution can work like this:

    1. Employee uploads receipt image using Power Apps.
    2. AI Builder extracts text from the image.
    3. Power Automate stores the extracted text in Dataverse.
    4. The finance team reviews the extracted information.
    5. An approval flow is triggered for expense validation.

    This helps reduce manual work and speeds up the expense claim process.

    Advantages of Text Recognition

    • It reduces manual data entry.
    • It helps convert paper documents into digital text.
    • It can process images, scanned files, and PDF documents.
    • It supports intelligent automation in Power Apps and Power Automate.
    • It improves productivity in document-heavy business processes.
    • It can help make scanned documents searchable and reusable.

    Limitations of Text Recognition

    • Blurry images may reduce recognition accuracy.
    • Poor lighting can affect text detection.
    • Unclear handwriting may not be recognized correctly.
    • Complex document layouts may require additional processing.
    • The extracted text may need human review for business-critical processes.

    Best Practices for Text Recognition

    Best Practice Explanation
    Use clear images Clear and sharp images improve text recognition accuracy.
    Avoid shadows Shadows can make text difficult for OCR to read.
    Use proper lighting Good lighting helps the model detect characters clearly.
    Keep document straight Skewed or tilted documents can reduce accuracy.
    Review extracted text Important business data should be reviewed before final use.
    Combine with business rules Use validation rules in Power Automate or Power Apps to check extracted data.

    Text Recognition Output

    The output of Text Recognition is the text detected from the uploaded file. The output can be used in different ways depending on business requirements.

    • Display extracted text in Power Apps
    • Store extracted text in Dataverse
    • Save extracted text in SharePoint list
    • Send extracted text through email or Teams notification
    • Use extracted text for search and reporting
    • Pass extracted text to another workflow or approval process

    Simple Practical Example

    A school receives scanned admission forms from students. The administration team wants to extract text from those scanned forms and store the information digitally.

    1. The scanned admission form is uploaded to SharePoint.
    2. Power Automate detects the new file.
    3. AI Builder Text Recognition extracts text from the form.
    4. The extracted text is stored in Dataverse.
    5. The admin team reviews and verifies the information.

    This reduces manual typing and makes the admission process faster.

    Text Recognition vs Object Detection

    Point Text Recognition Object Detection
    Main Purpose Reads and extracts text from images or documents. Detects objects inside images.
    Input Images, scanned documents, PDFs, receipts, forms. Images containing objects, products, tools, or assets.
    Output Extracted text. Detected object name, count, and location.
    Example Read text from an invoice image. Detect products on a store shelf.

    Important Points to Remember

    • Text Recognition is used to extract text from images and documents.
    • It uses OCR technology.
    • It can recognize printed and handwritten text depending on quality and supported scenarios.
    • It is available as a prebuilt model in AI Builder.
    • It can be used in Power Apps and Power Automate.
    • It is useful for document automation, receipt processing, and scanned file digitization.

    Conclusion

    Text Recognition in AI Builder is a powerful intelligent automation feature that helps organizations extract text from images, scanned documents, and PDF files. It reduces manual typing, improves business productivity, and supports digital transformation by converting image-based text into usable digital text.

    When combined with Power Apps, Power Automate, Dataverse, and SharePoint, Text Recognition can be used to build smart automation solutions for finance, education, HR, logistics, customer service, and many other business areas.

    Microsoft Power Platform Mastery