OCR (Optical Character Recognition) is the technology that converts written text in an image — printed or handwritten — into text a computer can process, search, and edit. It takes a scanned book page, a photo of an invoice, or a PDF and recognizes the letters inside, turning them into plain text. In this guide you will learn how OCR works and how to convert a document to text step by step using open-source tools.
What Is OCR and How Does It Work?
Modern OCR systems usually have two stages. The first stage is text detection: it finds where text is located in the image, identifying line and word regions. The second stage is text recognition: it predicts the characters in each detected region. Older systems used rule-based approaches that matched letters to templates; today, deep-learning models such as LSTM and transformers deliver far higher accuracy. The output is typically plain text, hOCR with coordinate data, or a searchable PDF.
Step 1: Choose the Right OCR Tool
Pick an engine based on your needs. For clean, high-resolution printed documents, the fast and lightweight Tesseract is ideal. For complex layouts, academic papers, invoices, tables, and handwriting, Surya OCR offers the highest accuracy. For real-world imagery such as photos, signs, and license plates, EasyOCR is strong. If you need mobile or edge deployment with table extraction, PaddleOCR stands out with its lightweight models. All of them are open-source and free.
Step 2: Prepare the Image (Pre-processing)
OCR accuracy depends heavily on input quality. For the best results, convert the image to grayscale, increase contrast, and apply binarization if needed. Correct skewed scans (deskewing), reduce noise, and use at least 300 DPI resolution where possible. Low-resolution or blurry images significantly degrade accuracy even on the best engines. Good pre-processing often makes more difference than a stronger model.
Step 3: Set the Language and Parameters
Choosing the right language model noticeably improves accuracy. For Turkish documents, use the 'tur' language file for Tesseract and the ['tr'] reader for EasyOCR, so that special characters such as ç, ğ, ı, ö, ş, ü are recognized correctly. For multilingual documents you can load several languages together. If the document layout is complex (multiple columns, tables), prefer Surya or PaddleOCR, which provide layout analysis; otherwise the reading order may become jumbled.
Step 4: Run OCR and Get the Output
Run your chosen tool to extract the text. For example, in Python with Tesseract you can convert an image to text using pytesseract.image_to_string(image, lang='tur') via the pytesseract library. In EasyOCR, reader = easyocr.Reader(['tr']) followed by reader.readtext(image) returns coordinates, text, and a confidence score for each text region. You can save the output as plain text, JSON, or a searchable PDF.
Step 5: Verify and Improve the Result
Review the output and flag low-confidence regions. You can fix common errors (such as 0/O or 1/l confusion) with dictionary-based post-processing or simple rules. If accuracy is not good enough, revisit your pre-processing, capture a higher-resolution scan, or switch to a stronger engine (e.g. Surya OCR). For recurring document types, custom-trained models can improve accuracy even further.
What Can You Do with OCR?
OCR is used for archive and book digitization, searchable-PDF creation, invoice and receipt processing, ID and form reading, academic paper extraction, and accessibility (feeding text to screen readers). With open-source tools you can do all of this without cloud costs, processing privacy-sensitive documents locally. To see which tool suits you, check our open-source OCR tools comparison.
Frequently Asked Questions
Is OCR free? Yes — Tesseract, EasyOCR, and PaddleOCR are free and open-source under the permissive Apache 2.0 license, and Surya OCR under GPL-3.0. Does OCR support Turkish? Yes, all of these tools recognize Turkish characters; choosing the right language model improves accuracy. Can it recognize handwriting? Surya OCR offers the best handwriting performance; Tesseract's default models are weak at handwriting.