tekstintunnistus

Tekstintunnistus, Finnish for text recognition, is the process of converting images containing printed or handwritten text into machine-readable characters. In practice it is commonly referred to as OCR (optical character recognition). Tekstintunnistus is a central task in document analysis and digitalization, enabling searchable archives, editable text, and automated data extraction from books, forms, receipts, and other documents. In Finnish contexts the term is used alongside the broader field of computer vision and natural language processing.

Typical workflows start with image preprocessing to improve contrast and reduce noise, followed by layout analysis to identify zones such as columns, lines, and regions of text. Text detection localizes the areas containing characters, and character recognition converts images to characters. Modern systems often employ deep learning models, including convolutional neural networks for feature extraction and sequence models such as recurrent neural networks or transformers for recognition and language modeling. Post-processing with dictionaries and statistical models improves accuracy and spelling.

Applications include digitizing books, invoices, and historical documents, automatic form processing, license plate recognition, and accessibility technologies for visually impaired users. Output formats vary from plain text to structured representations like hOCR, ALTO XML, or PAGE XML, which preserve layout information for downstream processing. Multilingual support and handling of diacritics, fonts, and handwriting remain active research areas.

Challenges include noise, skew, uneven illumination, complex layouts, and handwritten or cursive text. Ongoing advances in deep learning and transformer-based architectures continue to improve accuracy, speed, and robustness, expanding tekstintunnistus applications across languages and domains.