  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.
