Robust and real-time stroke order evaluation using incremental stroke context for learners to write Kanji characters correctly

Abstract

Writing Kanji characters of Chinese origin in the correct stroke order and direction is still one of the important subjects in Japanese elementary education. So far, the stroke order evaluation was made by stroke-to-stroke matching without stroke context so that it was unrobust to Kanji characters having multiple similar strokes. Here, we employ shape context features around each feature point in not only conventional fan-shaped bins but also in square bins with applying a Gaussian function. We also propose simple incremental context and augmented context from future strokes. Our approach can judge whether the stroke order and direction are correct or not every time a new stroke is written on a tablet by matching a partially written Kanji pattern with the reference pattern written to the same number of strokes. Evaluation shows that the best-tuned method with square bins and the Gaussian function records the highest performance and correctly evaluates stroke order by 98.5% with the maximum time of 0.12 sec. /character for Kanji patterns after all strokes are written using an average desktop PC. The method is also shown to possess high reliability to detect wrong stroke order and direction incrementally every time after each stroke is written.