Handwriting Recognition (HWR) is the capability of computers and mobile devices to receive and interpret handwritten inputs. The inputs might be offline (scanned from paper documents, images, etc.) or online (sensed from the movement of pens on a special digitizer, for example). Cursive handwriting recognition is one of the most challenging topics in the field of image processing and pattern recognition. The same alphabet and word are written differently by each person. Additionally, the text written by the same person at different times has variances. The advancement of systems for recognizing cursive writing improves human-machine interaction. An artificial neural network is used to develop a system for recognizing characters in cursive handwriting offline. Each printed character’s characteristics are taken out, such as word splitting and resizing, and then sent into the neural network. The system is trained using online data sets that include texts written by various individuals.