OCR, or Optical Character Recognition, is a technology that converts images containing printed, handwritten, or typed text into machine-readable and editable data. It differs from other image processing technologies by specifically focusing on extracting and structuring textual information from physical or digital media. OCR typically operates in three stages: image preprocessing (correction, filtering, contrast adjustment), character detection and segmentation, and recognition using statistical models or neural networks. The main implication of OCR is that it makes previously inaccessible content searchable and editable in digital form.
Use Cases and Examples
OCR is widely used for digitizing administrative documents, managing archives, automating the entry of invoices or mail, automatic license plate recognition, and accessibility solutions for the visually impaired. In banking and legal sectors, it speeds up document processing. In logistics, it helps read complex barcodes or handwritten delivery notes.
Main Software Tools, Libraries, and Frameworks
Key solutions include Tesseract (open source, initially developed by HP and maintained by Google), ABBYY FineReader (a well-known commercial solution), Google Cloud Vision OCR and Amazon Textract (cloud services), EasyOCR, PaddleOCR, Kraken (specialized in historical manuscripts), and modules in office suites (Adobe Acrobat, Microsoft OneNote).
Recent Developments, Evolutions, and Trends
The integration of deep learning has greatly improved accuracy, especially for handwritten or degraded documents. Multilingual and context-aware OCR models are advancing, as is the automation of complex documents (invoices, structured forms). OCR is now part of data extraction pipelines, semantic analysis, and Robotic Process Automation (RPA), paving the way for more intelligent and interactive applications.
