Image-Based Radical Identification in Chinese Characters

Abstract

The Chinese writing system, known as hanzi or Han character, is fundamentally pictographic, composed of clusters of strokes. Nowadays, there are over 85,000 individual characters, making it difficult even for a native speaker to recognize the precise meaning of everything one reads. However, specific clusters of strokes known as indexing radicals provide the semantic information of the whole character or even of an entire family of characters, are golden features in entry indexing in dictionaries and are essential in learning the Chinese language as a first or second idiom. Therefore, this work aims to identify the indexing radical of a hanzi from a picture through a convolutional neural network model with two layers and 15 classes. The model was validated for three calligraphy styles and presented an average F-score of ∼95.7% to classify 15 radicals within the known styles. For unknown fonts, the F-score varied according to the overall calligraphy size, thickness, and stroke nature and reached ∼83.0% for the best scenario. Subsequently, the model was evaluated on five ancient Chinese poems with a random set of hanzi, resulting in average F-scores of ∼86.0% and ∼61.4% disregarding and regarding the unknown indexing radicals, respectively.