Classification of Chinese Handwritten Numbers with Labeled Projective Dictionary Pair Learning
This addresses the problem of efficient and accurate handwritten number classification for applications like document digitization, though it appears incremental as it builds on existing dictionary learning approaches.
The paper tackled the challenge of simultaneously maximizing discriminability and sparse-representability in dictionary learning for image classification by designing class-specific dictionaries incorporating discriminability, sparsity, and classification error metrics, achieving ~98% accuracy on Chinese handwritten numbers with fewer parameters than deep learning methods.
Dictionary learning is a cornerstone of image classification. We set out to address a longstanding challenge in using dictionary learning for classification; that is to simultaneously maximise the discriminability and sparse-representability power of the learned dictionaries. Upon this premise, we designed class-specific dictionaries incorporating three factors: discriminability, sparsity and classification error. We integrated these metrics into a unified cost function and adopted a new feature space, i.e., histogram of oriented gradients (HOG), to generate the dictionary atoms. The rationale of using HOG features for designing the dictionaries is their strength in describing fine details of crowded images. The results of applying the proposed method in the classification of Chinese handwritten numbers demonstrated enhanced classification performance $(\sim98\%)$ compared to state-of-the-art deep learning techniques (i.e., SqueezeNet, GoogLeNet and MobileNetV2), but with a fraction of parameters. Furthermore, combination of the HOG features with dictionary learning enhances the accuracy by $11\%$ compared to the case where only pixel domain data are used. These results were supported when the proposed method was applied to both Arabic and English handwritten number databases.