Automatic gender detection
One of the most consequential creations in the human evolution phase is handwriting. Due to writing, today we are conveying our reflections, making business pacts, rendering an understandable world and making hitherto tasks austerer. Determining gender using offline handwriting is an applied research problem in forensics, psychology, and security applications, and with technological evolution, the need is growing. The general problem of gender detection from handwriting poses many difficulties resulting from interpersonal and intrapersonal differences.
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Several fields of study can benefit from a large, structured, and accurate dataset of historical figures. Due to a lack of such a dataset, in this paper, we aim to use machine learning and text mining models to collect, predict, and cleanse online data with a focus on age and gender. We developed a five-step method and inferred birth and death years, binary gender, and occupation from community-submitted data to all language versions of the Wikipedia project.
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SDTwittC consists of 200 authors evenly balanced by gender (100 for each). We identified the gender of the tweeters via their names and profile pictures. As potential copy-and-paste texts, both tweets and retweets are discarded in the first place. Only replies are compiled. The number of replies for each author varies from hundreds to thousands. Male authors produced 233926 replies whereas 219740 replies are generated by the female group
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