OCR (Optical Character Recognition)

Optical Character Recognition (OCR) is a technology that enables the conversion of text from images or scanned documents into a machine-readable format. The process involves several steps: first, the image is analyzed to identify text areas. Subsequently, individual characters are recognized, often through pattern recognition or neural networks. Finally, the recognized text is output, typically in a searchable and editable format like TXT, PDF/A, or XML. OCR is crucial for digitizing documents and making information originally only available in printed form accessible.

For marketing and CTO leads, OCR offers immense benefits in digitizing and automating business processes. It enables the extraction of customer data from forms, the analysis of receipts for expense reports, or the conversion of print materials into digital content for content marketing. This improves data availability, reduces manual input, and accelerates processes, leading to efficiency gains and better analytical capabilities. Compliance in archiving is also supported.

A B2B company receives numerous invoices and purchase orders daily via postal mail as paper documents. By using OCR, these documents are automatically scanned, relevant text (invoice number, amount, shipping address) is extracted, and transferred to the company's ERP system. This automates data entry, accelerates accounting processes, and minimizes sources of error.

OCR dates back to 1914 (Goldberg patent). Tesseract (HP, 1985; Google, 2006) became the open-source standard. Modern OCR uses deep learning (EasyOCR, PaddleOCR). Multimodal models (GPT-5, Gemini) can increasingly solve OCR tasks directly.

• Tesseract OCRDocumentation
• Google Document AIDocumentation