A deep learning framework for Text-independent Writer Identification

Congrats and thanks to Dr. Javidi for her collaboration on our recently accepted paper with the title of "A deep learning framework for Text-independent Writer Identification" in prestigious peer-reviewed journal of Engineering Applications of Artificial Intelligence (EAAI).



Handwriting Writer Identification (HWI) refers to the process of handwriting text image analysis to identify the authorship of the documents. It has yielded promising results in various applications, including digital forensics, criminal purposes, exploring the writer of historical documents, etc. The complexity of the text image, especially in images with various handwriting makes the writer identification difficult. In this work, we propose an end-to-end system that relies on a straightforward yet well-designed deep network and very efficient feature extraction, emphasizing feature engineering. Our system is an extended version of ResNet by conjugating deep residual networks and a new traditional yet high-quality handwriting descriptor towards handwriting analysis. Our descriptor analyzes the handwriting thickness as a preliminary and essential feature for human handwriting characteristics. Our approach can also provide text-independent writer identification that we do not need to have the same handwriting content for learning our model. The proposed approach is evaluated and achieved consistent results on four public and well-known datasets of IAM, Firemaker, CVL, and CERUG-EN. We empirically demonstrate that our conjugated network outperforms the original ResNet, and it can work well for real-world applications in which patches with few letters exist.

Paper is open access from following link till October 17, 2020: https://authors.elsevier.com/a/1berB3OWJ8wW9-


Code and Explanations

This project source files which are programmed with Python and Matlab can be found in the following.

Source Code

 Please cite our paper if you find it useful

Malihe Javidi, Mahdi Jampour,
"A deep learning framework for text-independent writer identification,"
Engineering Applications of Artificial Intelligence, Volume 95, 2020,

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