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• Automatic handwritten analysis system has been developed to detect the gender of the writer. • Machine learning methods have been used to extract set of gender-related features. • Large Arabic dataset was collected to conduct the experiment of the gender detection system. • Novel set of features were chosen with the consultation of a graphologist and psychologist. • Benchmark dataset was used to compare the proposed detection system with another researcher works. Gender detection from handwritten documents is a crucial research area in many disciplines such as psychology, pyelography, graphology, and forensic analysis. Furthermore, this task is challenging due to the high similarity and overlap between individuals' handwriting. The performance of the document recognition and analysis systems, depends on the extracted features from handwritten documents, which can be a challenging task as this depends on extracting the most relevant information from row text. In this paper, a set of gender-related features suggested by a graphologist, to detect the gender of the writers, have been proposed. These features include margins, space between words, pen-pressure and handwriting irregularity. Both SVM and ANN classifiers have been used to train, validate and test the proposed approach on two different data sets: our data set FSHS and ICDAR2013 dataset. The proposed method has achieved high classification rates of 94.7% and 97.1% using SVM and ANN respectively. Meanwhile, our method outperformed state-of-arts methods when applied to the ICDAR2013 dataset with classification rates of 91.4% and 92.5% using SVM and ANN respectively. [ABSTRACT FROM AUTHOR]

The main aim of this work is to compare Hindu and Arabic digits with respect to a machine’s ability to recognize them. This comparison is done on the raw representation (images) of the digits and on their features extracted using two feature selection methods. Three learning algorithms with different inductive biases were used in the comparison performed using the raw representation; two of them were also used to compare the digits using their extracted features. All classifiers gave better results for Hindu digits in both cases; when raw representation was used and when the selected features where used. The experiments also show that Hindu digits can be classified with better accuracy, higher confidence and using fewer features than Arabic digits. These results indicate that hand-written Hindu digits are actually easier to recognize than hand-written Arabic digits. The machine learning methods used in this work are instance based learning (the kNN algorithm), Naïve Bayesian and neural networks. The feature extraction methods we used were Fourier transformation and histograms. [ABSTRACT FROM AUTHOR]