Image to text. Upload a photo to extract text with structure preserved — headings, paragraphs, hierarchy, and special formatting.
Choose the type of analysis you want to perform on your image.
Select the AI vision model for analysis.
PNG, JPG or GIF files supported. You can upload multiple images.
Text Extraction is an AI tool that examines images to extract and format text with element hierarchy, relationships, structured content, headings, paragraphs, and special formatting. The tool helps individuals extract structured text from images, preserve document structure, and work with formatted text content. Text extraction requires understanding how to recognize text structure, how to preserve formatting, and how to organize extracted content logically. This tool combines knowledge of text recognition, document structure, formatting preservation, and content organization to provide comprehensive text extraction. It can analyze images containing structured text to extract content, identify hierarchy, and preserve formatting. The analysis helps you extract structured text, maintain document organization, and work with formatted content from visual sources.
Upload images containing structured text and the AI examines text aspects including text identification (recognizing all text elements in the image), hierarchy recognition (identifying text hierarchy like headings, subheadings, paragraphs), relationship analysis (understanding how text elements relate to each other), formatting preservation (maintaining formatting like bold, italic, lists, etc.), structure organization (organizing extracted text in logical structure), special formatting (identifying and preserving special formatting), and content structuring (creating structured text output). The analysis provides extracted text with preserved structure, identifies hierarchy, maintains formatting where possible, and offers structured content. The tool explains text extraction principles in accessible terms, helping individuals understand how structured text extraction works.
Upload it and the AI extracts everything readable, organized the way the original was: headings stay headings, paragraphs stay separate, lists keep their structure. The point is text you can paste into a document and keep working with, rather than one undifferentiated block you have to reformat by hand.
Both read text from images. OCR analysis focuses on faithful character extraction with formatting noted; text extraction puts the emphasis on structure, rebuilding hierarchy and the relationships between elements. For a dense structured document (a report page, a slide, a form), this tool's output needs less cleanup afterward.
It identifies tabular layouts and reconstructs rows and columns as structured text. Simple, clearly ruled tables come through well; dense spreadsheets photographed at an angle or with merged cells will need some manual cleanup. Shooting the table straight-on with all gridlines visible gives the cleanest reconstruction.
Character accuracy is high on sharp, well-lit images of printed text and degrades predictably with blur, glare, small fonts, and angle. Structure recognition is good on conventional layouts and weaker on creative or overlapping designs. Proofread anything load-bearing (numbers, names, legal text) before relying on it.
Digitizing printed handouts and book excerpts for notes, pulling quotes from screenshots, converting slide photos from lectures and meetings into editable outlines, rescuing text trapped in images where the original file is gone, and getting receipts or forms into a format you can actually work with.
Yes. Mixed-language documents extract with each language read natively, including non-Latin scripts, and the structure analysis treats them as one document rather than splitting by language. Accuracy varies a little by script and font, with the same rule as always: sharper image, better text.
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