Handwriting representation and recognition through a sparse projection and low-rank recovery framework

This paper proposes a Robust Principal Component Analysis (RPCA) based framework called Sparse Projection and Low-Rank Recovery (SPLRR) for representing and recognizing handwritings. SPLRR calculates a similarity preserving sparse projection for salient feature extraction and processing new data for classification in addition to delivering a low-rank principal component and identifying errors or missing pixel values from a given data matrix. As a result, SPLRR will be applicable for handwritten recovery, recognition and the applications requiring online computation. To encode the similarity between features in the learning process, the Cosine similarity based regularization is incorporated to the SPLRR formulation. The sparse projection and the lowest-rank components are calculated from a scalable convex minimization problem that can be efficiently solved in polynomial time. The effectiveness of the proposed SPLRR is examined by handwritten digital repairing, stroke correction and recognition on two real problems. Results show that SPLRR can deliver state-of-the-art results in handwriting representation.