Author identification by automatic learning

Frery, Jordan; Largeron, Christine; Juganaru-Mathieu, Mihaela

doi:10.1109/icdar.2015.7333748

Cited by 3 publications

(1 citation statement)

References 7 publications

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“…In Reference [4], counting methods, vote technique and supervised learning are proposed to identify if an author has written a given document or not. The counting method in combination with the vote technology delivers identification results of 70.7% but its effectiveness highly depends on the number of known documents.…”

Section: Of 25mentioning

confidence: 99%

Writer Identification Using Handwritten Cursive Texts and Single Character Words

et al. 2019

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One of the biometric methods in authentication systems is the writer verification/identification using password handwriting. The main objective of this paper is to present a robust writer verification system by using cursive texts as well as block letter words. To evaluate the system, two datasets have been used. One of them is called Secure Password DB 150, which is composed of 150 users with 18 samples of single character words per user. Another dataset is public and called IAM online handwriting database, and it is composed of 220 users of cursive text samples. Each sample has been defined by a set of features, composed of 67 geometrical, statistical, and temporal features. In order to get more discriminative information, two feature reduction methods have been applied, Fisher Score and Info Gain Attribute Evaluation. Finally, the classification system has been implemented by hold-out cross validation and k-folds cross validation strategies for three different classifiers, K-NN, Naïve Bayes and Bayes Net classifiers. Besides, it has been applied for verification and identification approaches. The best results of 95.38% correct classification are achieved by using the k-nearest neighbor classifier for single character DB. A feature reduction by Info Gain Attribute Evaluation improves the results for Naïve Bayes Classifier to 98.34% for IAM online handwriting DB. It is concluded that the set of features and its reduction are a strong selection for the based-password handwritten writer identification in comparison with the state-of-the-art.

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Section: Of 25mentioning

confidence: 99%

Writer Identification Using Handwritten Cursive Texts and Single Character Words

et al. 2019

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Chaotic vortex search algorithm: metaheuristic algorithm for feature selection

2021

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Analysis of effective techniques and algorithms in terms of “text mining” to predict the authorship in Albanian language

Harizaj,

Minga,

Harizaj

2023

CRJ

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Natural Language Processing has gained a special importance and development in recent years, where the analysis of written texts through various techniques of “text mining” and the extraction of all their features is a prerequisite to be used and be further implemented for various purposes. In this paper it will be compared some of the most effective techniques and algorithms in termsof “text mining”, to predict the authorship of a written text in the Albanian language, using for training the model, a fund of articles written by some of the most well- known bloggers of Albanian journalism. When talking about finding the authorship of a text it must kept in mind many important elements such as: number of sentences, sentence structure, number of words in a sentence, repetition of the same word, length of words used, frequency of the use of punctuation, literary figures used; elements which best display the unique narrative style for each author. This paper can serve as a good starting point to go further to its specific objective, predicting the authorship of an anonymous text, but also for other applications related to “text mining”, referring to the Albanian language.

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Author identification by automatic learning

Cited by 3 publications

References 7 publications

Writer Identification Using Handwritten Cursive Texts and Single Character Words

Writer Identification Using Handwritten Cursive Texts and Single Character Words

Chaotic vortex search algorithm: metaheuristic algorithm for feature selection

Analysis of effective techniques and algorithms in terms of “text mining” to predict the authorship in Albanian language

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