A robust script independent handwriting system for gender identification

Gender identification at the word level in a multi-script environment is challenging due to variations posed by free-style handwriting of individuals and geographical differences in writing styles. This paper presents a new approach, Multi-Orientation-Scale Gabor Response Fusion (MOSGF), for gender identification at the word level using handwritten text. Our method has two steps: (i) word segmentation from unconstrained lines and (ii) gender identification at the word level. In the first step, the method explores the number of zero crossing points and gradient information for word segmentation from handwritten text lines. In the second step, employs Gabor responses at different orientations and scales to detect fine details in female and male handwriting. For each Gabor response, the proposed model estimates the correlation between average templates of all Gabor responses and the individual Gabor response to extract global consistency in writing. To strengthen correlation features, the proposed method uses the Mahalanobis distance measure, which extracts local similarity. Further, the proposed approach fuses correlation coefficient and distance-based features in a novel way. The fused features are then fed to a Neural Network (NN) for gender identification. Experiments on our dataset, which comprises Roman (English), Chinese, Farsi (Persian), Arabic, and Indian scripts, and a benchmark dataset, namely, IAM which includes English text, KHATT which includes Arabic, and QUWI which includes both English and Arabic, show that the proposed system outperforms the existing methods in terms of word segmentation and gender identification.