A new method for writer identification based on historical documents

Gattal, Abdeljalil; Djeddi, Chawki; Abbas, Faycel; Siddiqi, Imran; Brahim, Bouderah

doi:10.1515/jisys-2022-0244

Cited by 5 publications

(3 citation statements)

References 60 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Writer retrieval is of core interest for historians, librarians, paleographers [2], law enforcement [3], or fraud prevention [4]. Due to the inherent scale of finding individual authors in a sea of reference documents and the extreme complexity of determining whether two documents were written by the same person, automated mechanisms for finding the correct writers have to be employed to solve writer retrieval at any meaningful scale.…”

Section: Introductionmentioning

confidence: 99%

Evaluating learned feature aggregators for writer retrieval

Mattick,

Mayr,

Seuret

et al. 2024

IJDAR

View full text Add to dashboard Cite

Transformers have emerged as the leading methods in natural language processing, computer vision, and multi-modal applications due to their ability to capture complex relationships and dependencies in data. In this study, we explore the potential of transformers as feature aggregators in the context of patch-based writer retrieval, with the objective of improving the quality of writer retrieval by effectively summarizing the relevant features from image patches. Our investigation underscores the complexity of leveraging transformers as feature aggregators in patch-based writer retrieval. While we have experimented with various model configurations, augmentations, and learning objectives, the performance of transformers in this task has room for improvement. This observation highlights the challenges in this domain and emphasizes the need for further research to enhance their effectiveness. By shedding light on the limitations of transformers in this context, our study contributes to the growing body of knowledge in the field of writer retrieval and provides valuable insights for future research and development in this area.

show abstract

Section: Introductionmentioning

confidence: 99%

Evaluating learned feature aggregators for writer retrieval

Mattick,

Mayr,

Seuret

et al. 2024

IJDAR

View full text Add to dashboard Cite

show abstract

“…The system uses the proposed features to train a k-means clustering algorithm to construct a codebook of size K. The method uses occurrence histograms of the extracted features in the codebook to create a final feature vector for each handwritten document. The work in [20] exploits the textural information in handwriting to characterize writers from historical documents. The authors use oBIF (oriented Basic Image Features) and hinge features and introduce a novel moment-based matching method to compare the feature vectors extracted from writing samples.…”

Section: Introductionmentioning

confidence: 99%

Short Words for Writer Identification Using Neural Networks

Κούκιου

2023

Applied Sciences

View full text Add to dashboard Cite

In biometrics, it is desirable to distinguish a person using only a short sample of his handwriting. This problem is treated in the present work using only a short word with three letters. It is shown that short words can contribute to high-performance writer identification if line characteristics are extracted using morphological directional transformations. Thus, directional morphological structuring elements are used as a tool for extracting this kind of information with the morphological opening operation. The line characteristics are organized based on Markov chains so that the elements of the transition matrix are used as feature vectors for identification. The Markov chains describe the alternation in the directional line features along the word. The analysis of the feature space is carried out using the Fisher linear discriminant method. The identification performance is assessed using neural networks, where the simplest neural structures are sought. The capabilities of these simple neural structures are investigated theoretically concerning the achieved separability into the feature space. The identification capabilities of the neural networks are further assessed using the leave-one-out method. It is proved that the neural methods achieve identification performance that approaches 100%. The significance of the proposed method is that it is the only one in the literature that presents high identification performance using only one short word. Furthermore, the features used as well as the classifiers are simple and robust. The method is independent of the language used regardless of the direction of writing. The NIST database is used for extracting short-length words having only three letters each.

show abstract

“…Researchers can train models to classify texts into predefined categories, such as religious genres, theological concepts, or historical periods. These models can also be utilized for predictive tasks, such as predicting the authorship or dating of religious texts, thereby yielding valuable insights into the historical and literary dimensions of religious traditions (Gattal et al 2023).…”

mentioning

confidence: 99%

The Role of Artificial Intelligence in the Study of the Psychology of Religion

Alkhouri

2024

Religions

View full text Add to dashboard Cite

The study of the psychology of religion encompasses various aspects of human experiences and beliefs, including the influence of emerging technologies such as artificial intelligence (AI). This article aims to examine the impact of AI on religious practices and rituals, highlighting its potential to reshape how individuals engage with spirituality. By exploring AI-powered religious applications, virtual communities, and online services, we seek to understand the transformation of traditional religious practices and raise important questions about authenticity, inclusiveness, and the role of technology in the psychology of religious contexts. Moreover, ethical considerations and challenges arising from the integration of AI into religion will be addressed. As researchers delve into this intersection, it is crucial to strike a balance between technological advancements and preserving the fundamental aspects of spirituality, personal growth, and genuine human connection. This article contributes to the existing literature by shedding light on the potential implications of AI in the realm of religious experiences, calling for further exploration of its ethical dimensions and unintended consequences. Ultimately, understanding the influence of AI on the psychology of religion prompts us to reflect on the nature of spirituality, belief formation, and the human experience itself.

show abstract

A new method for writer identification based on historical documents

Cited by 5 publications

References 60 publications

Evaluating learned feature aggregators for writer retrieval

Evaluating learned feature aggregators for writer retrieval

Short Words for Writer Identification Using Neural Networks

The Role of Artificial Intelligence in the Study of the Psychology of Religion

Contact Info

Product

Resources

About