Charaktererkennung

Charaktererkennung, also known as character recognition, is a technology that enables computers to identify and interpret human handwriting or printed text. This process involves several stages, beginning with image acquisition where the text is captured as a digital image. Following this, preprocessing steps are applied to enhance the image quality, such as noise reduction, binarization (converting the image to black and white), and deskewing to correct any tilting.

The core of character recognition lies in feature extraction, where distinct characteristics of each character are identified. These features can include loops, lines, intersections, and curves. Once features are extracted, a classification algorithm is used to compare these features against a database of known character patterns. Common algorithms include neural networks, support vector machines, and template matching.

Charaktererkennung has numerous applications. Optical Character Recognition (OCR) is a prominent example, used for digitizing documents, reading checks, and processing forms. Handwriting Recognition (HWR) is employed in devices like smartphones and tablets for converting handwritten notes into digital text, and in postal services for reading addresses. The accuracy of character recognition systems has significantly improved over the years, making them a valuable tool in various fields, from automation to accessibility. Ongoing research focuses on improving recognition rates for challenging handwriting styles and low-quality images.