Natural Scene Character Recognition Using Robust PCA and Sparse Representation

TL;DR

This paper introduces a novel approach for natural scene character recognition that combines robust PCA for denoising, HOG features for image representation, and sparse representation for classification, demonstrating competitive results on multiple datasets.

Contribution

The paper presents a new method integrating robust PCA and sparse representation for improved scene character recognition performance.

Findings

Achieved competitive accuracy on four public datasets.

Effectively denoised images using robust PCA.

Outperformed some existing methods in recognition tasks.

Abstract

Natural scene character recognition is challenging due to the cluttered background, which is hard to separate from text. In this paper, we propose a novel method for robust scene character recognition. Specifically, we first use robust principal component analysis (PCA) to denoise character image by recovering the missing low-rank component and filtering out the sparse noise term, and then use a simple Histogram of oriented Gradient (HOG) to perform image feature extraction, and finally, use a sparse representation based classifier for recognition. In experiments on four public datasets, namely the Char74K dataset, ICADAR 2003 robust reading dataset, Street View Text (SVT) dataset and IIIT5K-word dataset, our method was demonstrated to be competitive with the state-of-the-art methods.