Tekensherkenning

Tekensherkenning, often translated as character recognition, is a field within artificial intelligence and computer vision that focuses on enabling computers to identify and interpret characters within digital images or scanned documents. This technology is crucial for automating processes that traditionally required human reading and data entry.

The process of tekensherkenning typically involves several stages. First, the image undergoes preprocessing, which might include noise reduction, binarization (converting the image to black and white), and de-skewing to correct for any tilting. Next, segmentation is performed to separate individual characters or symbols from each other and from the background. Once segmented, each character is then analyzed. This analysis can be done using various methods, such as template matching, where a character is compared to a predefined set of templates, or feature extraction, where algorithms identify key characteristics of the character, like curves, lines, and intersections. Finally, a classification algorithm assigns a label to the recognized character.

Common applications of tekensherkenning include optical character recognition (OCR) for digitizing printed text, handwriting recognition for processing handwritten forms and notes, and license plate recognition in traffic management systems. The accuracy and effectiveness of tekensherkenning systems have significantly improved over the years due to advancements in machine learning, particularly deep learning techniques.