Converting Handwritten Text to Editable Format via Gesture Recognition for Education
Selena He, Meng Han, Nidhibahen Patel, Zhigang Li · 2020
In this project, we present a real-time Internet of Things (loT)-based system to convert handwritten text into editable format by implementing Hand Gesture Recognition (HGR) with Raspberry Pi for classroom education. HGR is implemented using edge detection algorithm and it is used to reduce computational complexity and improve the efficiency of the system. Raspberry Pi is used to retrieve and perceive HGR and to build a smart classroom via loT technologies. Handwritten images are converted into editable format by using OpenCV and machine learning algorithms. In the text conversion, recognition of uppercase and lowercase alphabets, numbers, special characters, mathematical symbols, equations, and figures are included with recognition of word, lines, blocks, and paragraphs. With the help of Raspberry Pi and IoT technologies, students can access the editable format of lecture notes via a desktop application which helps students to edit and share notes and images according to their necessity. Implementation details and comprehensive evaluations of the system are summarized in the paper.