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Highlights • A novel system for off-line text-independent writer identification is proposed. • Effective feature extraction and dimensionality reduction methods are proposed. • Experiments are conducted on four challenging datasets (two English and two Arabic). • The proposed system provides high identification rates. • The proposed system demonstrates good performance stability across all the datasets. Abstract Feature engineering forms an important component of machine learning and pattern recognition. It is a fundamental process for off-line writer identification of handwritten documents, which continues to be an interesting subject of research in various forensic and authentication areas. In this work, we propose an efficient, yet computationally and conceptually simple framework for off-line text independent writer identification using local textural features in characterizing the writing style of each writer. These include Local Binary Patterns (LBP), Local Ternary Patterns (LTP), and Local Phase Quantization (LPQ). Our approach focuses on exploiting the writing images at small observation regions where a set of connected component sub-images are cropped and extracted from each handwriting sample (document or set of word/text line images). These connected components are seen as texture images where each one of them is subjected to feature extraction using LBP, LPQ, or LTP. Then, a histogram sequence concatenation is applied to the feature image after dimensionality reduction followed by image subdivision into a number of non-overlapping regions. For classification, the 1-NN (Nearest Neighbor) classifier is used to identify the writer of the questioned samples based on the dissimilarity of feature vectors computed from all components in the writing. Experiments on IFN/ENIT (411 writers/Arabic), AHTID/MW (53 writers/Arabic), CVL (309 writers/English), and IAM (657 writers/English) databases demonstrate that our proposed system outperforms old and recent state-of-the-art writer identification systems on Arabic script, and demonstrates a competitive performance on English ones. [ABSTRACT FROM AUTHOR]

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 × 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 × 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. [ABSTRACT FROM AUTHOR]