Block wise local binary count for off-Line text-independent writer identification

Feature engineering is fundamental in applied machine learning. It plays a major role in writer identification of handwritten documents, which has been an active area of research in the literature. In this paper, we propose a conceptually simple, yet high-quality and computationally efficient descriptor referred to as block wise local binary count (BW-LBC) for offline text independent writer identification of handwritten documents. The proposed BW-LBC operator characterizes the writing style of each writer by a set of histograms calculated from all the connected components in the writing. Each histogram is constructed by calculating the occurrence distribution of pixels corresponding to the writing within small blocks in each connected component extracted and cropped from the input handwriting sample (document or set of words/text lines). Specifically, for a given connected component divided into N x N non-overlapping blocks, the appearance probability of writing pixels in the block number i corresponds to the histogram bin number i in the produced corresponding histogram of N x N bins. The samples are classified according to their normalized histogram feature vectors through the nearest-neighbor rule (1-NN) using the Hamming distance. Extensive experiments performed on four challenging handwritten databases (IFN/ENIT, AHTID/MW, CVL and IAM) containing handwritten texts in Arabic and English languages, show that the proposed system using the BW-LBC operator demonstrates superior performance on the Arabic databases (i.e., AHTID/MW and IFN/ENIT) and competitive performance on the English scripts compared to the old and recent state-of-the-art writer identification approaches. (C) 2017 Elsevier Ltd. All rights reserved.