Abstract
Handwriting based gender identification at the word level is challenging due to free style writing, use of different scripts, and inadequate information. This paper presents a new method based on Multi-Gabor Response (MGR) fusion for gender identification at the word level. It first explores weighted-gradient features for word segmentation from text line images. For each word, the proposed method obtains eight Gabor response images. Then it performs sliding window operation over MGR images to smooth the values. For each smoothed MGR images, we perform fusion operation that chooses the Gabor response value which contributes to the highest peak in the histogram. This process results in a feature matrix, which is fed to CNN for gender identification. Experimental results on our dataset (multi scripts) apart from English, and benchmark databases, namely, IAM, KHATT, and QUWI, which contain handwritten English and Arabic text, show that the proposed method outperforms the existing methods.
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Acknowledgement
This work was supported by the Natural Science Foundation of China under Grant 61672273 and Grant 61832008, and the Science Foundation for Distinguished Young Scholars of Jiangsu under Grant BK20160021.
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Asadzadeh Kaljahi, M., Vidya Varshini, P.V., Shivakumara, P., Pal, U., Lu, T., Guru, D.S. (2019). Word-Wise Handwriting Based Gender Identification Using Multi-Gabor Response Fusion. In: Sundaram, S., Harit, G. (eds) Document Analysis and Recognition. DAR 2018. Communications in Computer and Information Science, vol 1020. Springer, Singapore. https://doi.org/10.1007/978-981-13-9361-7_11
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DOI: https://doi.org/10.1007/978-981-13-9361-7_11
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