Image to Text Converter

Use the Image to Text converter to extract text from images with our OCR (Optical Character Recognition) Software, upload your image, click convert and get the extracted text in the format that suits you best.

Compatible formats:

Processing

Uploading files

About the Image to Text Converter

This is an online Free OCR tool that provides its users access to our A.I. powered OCR Software to convert scanned images into editable file formats or to simply just extract text from images. The tool has a simple intuitive user interface that enables users to upload an image in various formats such as JPG, JPEG, PNG, TIFF or PDF and convert it into text with high accuracy.

In order to achieve a high accuracy result when converting an image into text be sure to feed the Online OCR with high quality images or PDFs, by high quality we mean no blurry, grainy, shaky or noise polluted images, this way you ensure the high quality results that you are expecting from an Image to Text Converter.

How to Convert PDF to Word file

  • Upload your scanned files by clicking "Upload Files" button.
  • Select the editable file type you need from the "File Type" drop down.
  • Select the proper language from the "Language" drop down.
  • Click the "Convert" button to convert PDF to Word.

OCR Supported File Types

  • High-resolution Photographs (JPG)
  • Transparent Images (PNG)
  • Scanned Documents (PDF)
  • Complex Multi-page Files (TIFF)

How does OCR work?

  1. Image Acquisition:

    The process begins with the acquisition of an image containing text. This image can be scanned from physical documents, captured through cameras, or obtained from digital sources.

  2. Preprocessing:

    The acquired image may contain noise, artifacts, and variations in lighting and quality. Preprocessing techniques are applied to enhance the image quality, such as adjusting contrast, removing background noise, and sharpening edges, this greatly improves the OCR process.

  3. Text Detection:

    OCR software locates areas within the image that likely contain text. This involves identifying regions where the contrast between text and background is significant.

  4. Text Segmentation:

    The detected text regions are then segmented into individual characters or words. This step involves breaking down the connected components of the text into manageable units.

  5. Feature Extraction:

    For each segmented character or word, features are extracted. These features include patterns of lines, curves, edges, and angles that help distinguish one character from another.

  6. Character Recognition:

    OCR algorithms compare the extracted features of each character against a database of known characters and fonts. This is where machine learning and pattern recognition techniques come into play. Neural networks, Hidden Markov Models (HMM), or other algorithms are often used to match features to known characters.

  7. Postprocessing:

    After recognition, the OCR system may perform postprocessing steps to improve accuracy. This can involve correcting errors based on context, spell-checking, and handling ambiguous characters.

  8. Output Generation:

    The recognized characters are then converted into digital text format. Depending on the application, the output can be plain text, formatted text, or even structured data like tables.

  9. Linguistic Analysis (Optional):

    In some advanced OCR systems, linguistic analysis may be applied to understand the context of the recognized text. This can involve checking grammar, syntax, and semantics to ensure the accuracy of the output.

  10. Verification and Correction:

    Human verification and correction can be integrated to review and fix any errors that the OCR system might have made during the recognition process.

OCR Software Use Cases

  1. Convert PDF to Word

    The process begins with the acquisition of an image containing text. This image can be scanned from physical documents, captured through cameras, or obtained from digital sources.

  2. Education

    Research Papers: Students and researchers can use the tool to convert PDFs of research papers into editable text, making it easier to highlight, annotate, and cite relevant sections.

  3. Image into Text

    Data Entry: You have a printed document or a handwritten note that you want to convert into editable text. Capture an image of the document and use the OCR tool to extract the text.

  4. OCR Software

    Archiving: You have a collection of scanned documents, such as old letters or historical records, and you want to digitize them for easy access. The OCR software can convert these scanned images into searchable and editable text.

  5. PDF to Text Converter

    Research: You've downloaded a research paper in PDF format, but you only need to extract specific text or quotes. Use the PDF to text converter feature to extract the relevant information.

  6. Image OCR

    Language Translation: You're traveling and come across signs or menus in a foreign language. Take a picture with your phone, and use the image OCR feature to translate the text into your preferred language.

  7. Content Extraction

    Legal: In the legal field, you receive scanned copies of contracts or legal documents. Use the OCR tool to extract specific clauses or sections of text for analysis or reference.

  8. Accessibility

    Visually Impaired: Individuals with visual impairments can use the OCR tool to convert printed materials into text that can be read aloud by screen readers, improving accessibility.

  9. Content Creation

    Blogging: Bloggers and content creators can extract text from images or PDFs to incorporate into their articles or posts, saving time on manual typing.

  10. Inventory Management

    Inventory Lists: Businesses can scan handwritten inventory lists and use OCR to convert them into digital records for better inventory management.

  11. Data Extraction

    Receipts and Invoices: Small businesses can extract data from receipts and invoices by scanning them and using OCR to convert the printed information into digital records for accounting purposes.

  12. Language Learning

    Vocabulary Building: Language learners can use the image OCR feature to convert foreign language texts into their native language, helping them learn new words and phrases.

  13. Resume Parsing

    Recruitment: HR professionals can use OCR software to parse resumes, extracting key information like names, contact details, and qualifications into a structured database.

  14. Resume Parsing

    Legal Documents: Convert scanned contracts or agreements into editable text, then add digital signatures electronically, streamlining the signing process.

  15. Historical Document Preservation

    Archives: Museums, libraries, and historical societies can use OCR software to digitize and preserve aging documents, making them accessible to researchers and the public.


These use cases demonstrate the versatility of an online OCR tool, which can be applied across various industries and for both personal and professional purposes.