In today’s world, extracting text from images has become ubiquitous. From students digitizing lecture notes to business professionals converting scanned documents to casual users wanting to copy text from a photo or screenshot, converting images to text saves time, reduces manual typing, and enhances productivity. People today are spending less time digitizing and more time on activities that add more value to their workflow.
With advancements in technology and OCR, extract images to text today et a much faster rate than in the past. However, that tools and images are not created equal. This guide will answer all your questions about how OCR works, the best tools to use, and tips on accuracy, privacy, advanced methods, and how to use OCR in your everyday life.
Extracting text from images is the most advanced and comprehensive process of digitizing text. It is the best way to bypass excessive manual typing in order to increase productivity.
What Does It Mean to Extract Text from an Image?
Extracting text from an image is the process of converting text that is photo, scanned, screenshot, or graphic into editable and searchable digital text. This is done through OCR technology in which the device identifies the characters and words on the pages and, in some cases, identifies the text formatting.
OCR commonly works with the following types of images:
- Files scanned by a document scanner
- Image-based PDFs
- Screenshots captured from display screens
- Photos captured with mobile devices
- Interpreted infographics or illustrative posters
- Handwritten notes (this varies by tool)
OCR converts images into:
- Plain text outputs
- Edit-friendly documents (TXT, DOCX)
- Searchable PDFs
- Structured data in CSV format
- Integrated outputs with Google Docs, Notion, or Microsoft Word
Uses of Extracting Text from Images
Text extraction has many personal, educational, and business applications. Here are a few examples:
1. Saves Time and Reduces Manual Work (Typing)
Typing from a paper document, instead of OCR, takes considerable time. This tool instantly removes all the text from a document.
2. Document Accessibility
Digital text enables the document to be searched, edited, highlighted, and read by computer-generated screen readers.
3. Productivity
Documents, such as receipts, invoices, forms, and contracts, are processed at a much faster rate.
4. Information Organization
Students and researchers aid in the conversion of handwritten notes and pages from textbooks into a more organized and searchable format.
5. Preservation
OCR recovery works on texts from documents that are faded, scanned, or of historical importance.
How OCR Works: A Simple and Clear Breakdown
Even though OCR might feel overwhelming, the face value processes involved are simple and plausible. Once you drop an image into an OCR tool, the software activates a series of internal functions to comprehend and parse the text.
1. Image Preprocessing
The first recognition steps include:
- Converting the image to grayscale
- Removing background noise
- Adjusting contrast levels
- Skew and tilt correction
- Sharpening
All steps are geared towards improving accuracy within low quality images.
2. Character Segmentation
The tool splits the image along the following lines:
- Horizontal lines
- Words per line
- Individual characters
Segmentation of the image line-by-line, word-by-word, and character-by-character is essential to enable accurate selection and identification.
3. Feature Extraction
The OCR software looks at characters and their shapes, curves, and edges, and compares each with the character patterns already stored in the database or learned within the system.
4. Text Recognition
Given the following, the software is able to identify characters:
- Pattern recognition
- Machine learning
- Language modelling
- Neural network predictions
AI is used in most modern OCR tools to draw inferences based on the analysed context, therefore accurate resolution of blurry and unclear characters is possible.
5. Post-Processing
This stage focuses on improving the accuracy of the text by:
- Verifying the spelling
- Rectifying formatting issues
- Applying grammatical rules
- Using contextual dictionaries
Factors that Affect OCR Accuracy
Not every conversion of image-to-text is completed flawlessly. The results hinges on the input image, among other things.
1. Image Resolution
Greater clarity is available with higher resolutions, making it easier for OCR software to recognize text. Images of lower than 100 DPI will lower accuracy.
2. Lighting and Contrast
Low lighting and low contrast can make it difficult for the OCR program to read the text, or worse, skip entire words.
3. Text Alignment
OCR software works best with horizontally aligned, straight text. Preprocessing is required for curved or tilted text, and is often the case with pictures.
4. Font Type
Recognizing simpler, clear typefaces such as Arial and Times New Roman is significantly easier than more decorative or handwritten fonts.
5. Background Noise
Character detection can be interfered with by the presence of patterns, shadows, or other graphics behind the text.
How to Prepare Images for the Best OCR Results
The best practices outlined below will help you to ensure maximum accuracy for text extraction from images.
1. Ensure Good Lighting
When photographing documents, be sure to not have shadows, glare, or reflections that will obstruct visibility.
2. Keep the Camera Steady
Text may become blurred making it harder to read. Stick to a steady surface and a tripod if necessary.
3. Crop Unnecessary Background
When the software has to deal with borders and excess background it becomes less focused on the text and a lot slower.
4. Increase Contrast
Pre-edit the image to clarify the text before running the image through the OCR tools.
5. Straighten the Image
Make sure the text is horizontal and it’s easy to read.
6. Use High-Resolution Images
Digitally scanned documents should be a minimum of 300 DPI.
Best Ways to Extract Text from Images
You have a number of options to choose from depending on your requirements and available resources.
Online OCR Tools
Without installation, image-to-text conversion takes a few clicks on the internet. They’re suitable for simple, unclassified documents.
Commonly noted features include:
- You can upload and process images in multiple formats
- Processing images is fast and uploads are quick
- OCR technology is available in multiple languages
- Different outputs for texts are available
- Documents can be converted from images into text in bulk
OCR Software for Desktop and Offline Use
These are the best options when you have high volumes of documents or when privacy is a concern. Offline OCR guarantees:
- Files are never uploaded
- Processing for large volumes of files is quicker
- More privacy
Some options include:
- ABBYY FineReader
- OneNote OCR
- Tesseract OCR
- Windows PowerToys Text Extractor
OCR Mobile Applications
Mobile OCR applications are convenient for document scanning when you are away from your computer. They offer:
- Automatic edge detection
- Correction of perspective
- Recognition of handwritten text
- Integration with the cloud
Some of these include:
- Google Lens
- Microsoft Lens
- Adobe Scan
OCR API and Tools for Developers
OCR APIs are used by developers to insert the functionality of text recognition into their applications. Some of the more used APIs are:
- Google Vision
- Microsoft Azure OCR
- Tesseract with Python
- AWS Textract
Step-by-Step Guide to Extracting Text from an Image
These steps will be applicable to whatever method you decide to use.
Step 1: Decide on an OCR Tool
You can use an online tool, a piece of offline software, or a mobile app.
Step 2: Insert Your Image
Commonly accepted formats are JPG, PNG, BMP, TIFF, and PDF.
Step 3: Select Language and Format of Output
Accuracy improves significantly with proper language selection.
Step 4: Start OCR Processing
Hit the Convert Extract or otherwise named button.
Step 5: Review and Edit the Extracted Text
OCR will not be flawless, and bad images will require more adjustments.
Step 6: Download or Export
Your output can be in TXT, DOCX, searchable PDF formats or shared with Google Docs, Notion, etc.
Privacy and Security Considerations When Using OCR
With OCR, you are likely uploading pictures to be processed OCR, which can lead to a security problem.
When to Avoid Online OCR
Do not use to upload documents with:
- ID documents
- Sensitive financial data
- Legal documents
- Sensitive documents belonging to a company
Best Practices for Data Without Security Risks
- OCR software running offline
- Tools with at least SSL encryption
- Auto-delete document extraction services
- Scanned documents with sensitive information should not be stored in the cloud
Common OCR Errors and How to Fix Them
Even good OCR can make a mistake every once in a while.
1. Mislocations
Similar-looking characters are confused with each other. For example:
- O vs 0
- I vs lowercase l
- B vs 8
Fix: Use spell checking or context correction.
2. Additional space or incorrect line
This is seen with images that aren’t very well aligned.
Fix: Manually reformat the extracted text.
3. Text that is not recognized
OCR software might skip text that is so faint or unclear.
Fix: Increase the contrast in the image so the text is more visible or get a new scan.
4. Language Misidentification
Using the wrong language setting is very problematic.
Solution: Setting the correct OCR language prior to conversion is the solution.
Advanced OCR Techniques for Better Performance
1. Image Pre-processing
Utilize:
- GIMP
- Photoshop
- Online Image Enhancer
Image processing before OCR will add clarity.
2. Machine Learning Based OCR
Some servies are able to contextualize and better refine responses through language models.
3. Recognizing Handwriting
Some sophisticated AI is able to detect cursive and messy handwriting. Veracity is always an issue.
4. Batch Mode Processing
Process full automation for large amounts of documents.
Extracting Text from Different Document Types
1. Extracting Text Available in Screenshots
OCR is easy to use when images contain text.
Hints:
- Try to avoid excessive cropping
- Keep the text properly aligned in the center
2. Extracting Text from Scanned PDFs
OCR is always required before image PDFs can be made searchable.
3. Extracting Text from Handwritten Notes
Legibility, and the style of handwriting, will affect OCR.
4. Extracting Text from Receipts and Bills
Faded ink is often used in receipts; maintain good lighting as the ink can be hard to see.
Real-World Use Cases for OCR
1. Business and Finance
Processing invoices and data entry are automated.
2. Education
Handwritten notes and pages from books are digitized.
3. Healthcare
Records that are kept on paper are digitized for data entry.
4. Legal
Old case files and contracts are digitized.
5. Research
Digitizing historical documents, manuscripts, and images.
Advanced Section: Implementing Tesseract with Python
Tesseract is one of the best open-source OCR engines.
Example of a Workflow
- Make sure to have Tesseract and the pytesseract library installed
- Use python to load the images
- Preprocess the image by converting it to greyscale and thresholding
- Send the image to Tesseract
- Store the output
This gives the capability to have complete automation and integration with other systems.
FAQS
1. Can OCR work with handwritten notes?
Yes, but it needs to be handwritten clearly and be legible. AI-based systems work better with cursive.
2. Is OCR error free?
In short, no. It varies. Quality of the image, the language used, and the font of the text all come into play. It will be better with high-quality images.
3. Can text be extracted from a screenshot?
Yes. Screenshots tend to work best because the text is usually clear and the image is high quality.
4. Is OCR safe to use with private documents?
Online services should not be used with sensitive documents. Offline tools should be used to secure documents better.
5. Does OCR work in multiple languages?
Yes, many of the more advanced tools support dozens of languages. These include Arabic, Chinese, and Hindi.
6. Is it possible to maintain the structure of the text?
Some tools have the ability to maintain the structure of tables and paragraphs. However, it is not uncommon to have imperfect forms.
Conclusion
Modern OCR technologies have advanced so far, that anyone, anywhere in the world, can convert images, documents, screens, or even handwritten notes into editable and searchable documents in a matter of seconds. The only thing that changes the quality of the result, is the type of OCR tools and the quality of the images.
We live in a world where there are almost an infinite amount of OCR tools that can convert images or documents from the web in a matter of seconds. We have OCR tools that can easily convert documents offline while keeping your documents secure, and we have OCR tools that offer mobile scanning. No matter the use case, there is an OCR tool built for your use case. The guide provided in the previous sections offers numerous methods that can be used to enhance productivity and efficiency in extracting text from documents.