To extract text from a PDF image, use a tool with OCR (optical character recognition) that reads the characters from the image and converts them into selectable, copyable text. AI-powered tools like Lido go further by organizing the extracted text into structured fields automatically. Standard copy-paste does not work on image-based PDFs because the file contains a picture of the page, not actual text.
When a PDF contains an image instead of real text, you cannot select, copy, or search the content. The file looks normal on screen, but the text is part of a photograph or scan. This is one of the most common frustrations people encounter with PDF documents.
This guide explains how to extract text from a PDF image using 5 different methods, from free tools to AI-powered platforms that produce structured output.
An image-based PDF is a file where the pages are stored as pictures rather than as text characters. This happens when a paper document is scanned, when a page is photographed with a phone camera, or when a PDF is created from an image file like a JPEG or PNG.
The difference matters because regular PDF tools can only work with text that is stored as characters in the file. When the text is part of an image, you need OCR technology to read the characters from the picture before you can do anything with them.
Open the PDF and try to click on a word. If your cursor changes to a text selection tool and you can highlight individual words, the PDF contains real text. If clicking does nothing and you cannot highlight anything, the PDF is image-based. You can also try Ctrl+F to search for a word you can see on the page. If the search finds nothing, the text is embedded in an image.
Upload the PDF to Google Drive, right-click the file, and select "Open with Google Docs." Google Docs runs OCR on the image and converts the content into an editable document. Once the conversion finishes, you can select and copy any text.
This method is completely free and works for simple pages with clear text. It handles single-column layouts and standard fonts well. It struggles with multi-column pages, tables, low-resolution scans, handwriting, and complex formatting. The converted text often loses its original structure, so tables come out as jumbled lines of text rather than organized rows and columns.
Adobe Acrobat Pro includes a "Recognize Text" feature (also called "Make Searchable") that runs OCR on image-based PDFs. After processing, the text becomes selectable and searchable within the PDF itself. You can then copy text normally or export the document to Word or Excel.
Acrobat Pro produces good results on clean, high-resolution scans with printed text. It recognizes multiple languages and preserves some formatting during export. At $22.99 per month, it is a reasonable option if you already use Acrobat for other PDF tasks. It is less effective on low-quality scans, photographed documents with shadows or skew, and handwritten text.
Several free tools can extract text from image-based PDFs. Tesseract (open-source, command-line), OnlineOCR.net (browser-based), and Microsoft's built-in OCR in the Windows Snipping Tool all read text from images without requiring a paid subscription.
Free OCR tools produce raw text output. They give you the characters on the page but do not organize them into fields, tables, or structured data. The output is a plain text stream that you need to manually sort through and format. For grabbing a few paragraphs from a simple page, free OCR is sufficient. For anything with tables or structured data, you need more capable tools.
For developers, the pytesseract library wraps the Tesseract OCR engine in Python. You can write a script that opens an image-based PDF, runs OCR on each page, and outputs the text to a file. Here is a basic example.
from pdf2image import convert_from_path
import pytesseract
pages = convert_from_path("scanned_document.pdf")
with open("output.txt", "w") as f:
for page in pages:
text = pytesseract.image_to_string(page)
f.write(text + "\n")This script converts each PDF page to an image, runs OCR on it, and writes the recognized text to a file. It works for batch processing and can be customized with Tesseract's language packs and configuration options. Like all OCR-only approaches, the output is raw text without structural organization.
AI tools like Lido combine OCR with machine learning to both read the text from the image and understand the document structure. Instead of producing a raw text dump, the AI identifies which text is a heading, which is a table cell, which is a field label, and which is a field value. The output is structured, labeled data.
This is the only method that produces clean, organized output from image-based PDFs without manual cleanup. It handles scanned documents, photographed pages, low-resolution images, faded text, and complex layouts including tables, forms, and multi-column pages.
Lido reads text from any image-based PDF and delivers structured output. Here is how to use it.
Drag and drop your image-based PDF into Lido. It works on scanned documents, photographed pages, screenshots, and any other image-based PDF regardless of resolution or quality.
Lido's AI runs OCR to read every character on the page, then analyzes the layout to understand the document structure. Tables are extracted as tables. Form fields are matched to their values. Line items are organized into rows. All of this happens automatically with no configuration.
Export the extracted text and data to Excel, Google Sheets, CSV, or QuickBooks. The output arrives structured and labeled, ready to use without manual reformatting.
Lido delivers 99%+ field-level accuracy on image-based PDFs and includes a 24-hour refinement window for corrections at no extra cost. It is SOC 2 Type II and HIPAA compliant. Start with 50 free pages to test it on your own scanned documents.
Higher resolution produces better accuracy. If you are scanning documents yourself, use at least 300 DPI. Lower resolutions force the OCR engine to guess at characters, which increases errors.
Straighten skewed pages before processing. Pages scanned at an angle or photographs taken at a tilt produce worse OCR results. Most scanning software has an auto-straighten option. Use it.
Avoid heavily compressed images. JPEG compression at low quality settings creates artifacts around characters that confuse OCR engines. When possible, use PNG or high-quality JPEG for your scanned images.
Check for existing text layers first. Some PDFs look image-based but actually have a hidden text layer underneath the image (created by previous OCR processing). Try Ctrl+F before assuming you need OCR. You may already have selectable text.
Now that you know how to extract text from a PDF image, you can handle scanned documents, photographs, and any other image-based PDF without getting stuck.
Use a tool with OCR to read the characters from the image. Free options include Google Docs, Tesseract, and OnlineOCR.net. For structured output (tables, forms, labeled fields), use an AI tool like Lido that combines OCR with document understanding.
Your PDF is likely image-based, meaning the pages are stored as pictures rather than as text characters. You need OCR to read the text from the image before you can copy it. Try opening the PDF with Google Docs or using an OCR tool.
Yes. Google Docs (upload to Google Drive, open with Docs) runs free OCR on scanned PDFs. Tesseract is a free open-source OCR engine. OnlineOCR.net is a free browser-based option. Free tools produce raw text without structure, so expect manual cleanup for tables or forms.
For raw text extraction, Tesseract and Adobe Acrobat Pro are both effective. For structured output where the text is organized into labeled fields and tables, AI tools like Lido produce the best results because they understand document layout in addition to reading characters.
Yes. The same OCR tools that work on scanned PDFs also work on photographed pages. Accuracy depends on image quality, lighting, and angle. Straight-on photos with good lighting produce the best results. AI tools like Lido handle low-quality photographs better than basic OCR because they use machine learning to interpret unclear characters.
Basic OCR tools cannot extract tables with structure intact. They produce raw text that loses row and column relationships. To extract a table from an image PDF with the structure preserved, use an AI tool like Lido that combines OCR with table detection and outputs organized rows and columns.
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