Quality Evaluation Approaches of the First Grade Children's Handwritting

Abstract

The evaluation of early school-aged children's handwritten symbols is a challenging problem. The teaching of handwriting is still an essential skill in effective written communication. There is a need for an automatic quality evaluation of handwritten symbols in order to assess the progress of children ability to write nice letters or other symbols. In our case, the letters are positioned in a well defined two-dimensional space, similar to the special notebooks children use in school when learning how to write. For this study, a Wacom system composed of a tablet and a digital pen is used for collecting the data that will be sent to the analysis and evaluation module. While the school-aged child writes a character, the pen transmits the (x,y) coordinates and the time t. Next, a normalization is needed in order to keep the same distance between neighboring pixels. Several approaches are investigated (e.g. normalization with/without interpolation). The coordinates sequence is transformed in a sequence of angles measured relatively to the X axis for each written character. They encode the changing directions during pen movement. An algorithm is used in order to detect if the overall shape of the written symbol is correct. Several parameters that characterise the written character are investigated (e.g. ,,centre of mass", height over width ratio, alignment errors etc.). Their correlation with subjective scores is verified. Several metrics are proposed based on spatial and temporal measurements. Next, the handwritten quality using the legibility, form size and alignment of the letters or digits is investigated. It is shown that a rough discrimination between proficient and non-proficient handwriting can be obtained by considering the size and space parameters. Our simulations have revealed the importance of good handwritten reference samples. The goal is to develop a calligraphic handwritting learning system designed for first grade or pre-school childrens. Further research is needed in order to address other aspects of an intelligent tutor.

Speech emotion recognition based on Graph-LSTM neural network

“…However, there are some shortcomings in the current stage, including 1) complex connections and a large number of redundant features in graph; 2) unstable processing and analysis of small datasets; 3) neglecting the speaker's information. The research focuses on adult speech, which is a lack of exploration of children's speech emotion recognition [47,48].…”