Giuseppe De Gregorio

• University of Salerno

We address the problem of estimating the tradeoff between the size of the training set and the performance of a KWS when used to assist the transcription of small collections of historical handwritten documents. As this application domain is characterized by a lack of data, and techniques such as transfer learning and data augmentation require more...

During recent years, there here has been a boom in terms of deep learning use for handwriting analysis and recognition. One main application for handwriting analysis is early detection and diagnosis in the health field. Unfortunately, most real case problems still suffer a scarcity of data, which makes difficult the use of deep learning-based model...

This paper aims to inspect the often neglected role of Graphical User Interfaces (GUI) in AI-based tools designed to assist in the transcription of handwritten documents. While the precision and recall of the handwritten word recognition have traditionally been the primary focus, we argue that the time parameter associated with the GUI, specificall...

The growth of digital libraries has yielded a large number of handwritten historical documents in the form of images, often accompanied by a digital transcription of the content. The ability to track the position of the words of the digital transcription in the images can be important both for the study of the document by humanities scholars and fo...

Libraries contain a large number of digital images of handwritten documents of historical and cultural interest, and digital transcriptions are also available for some of them. The ability to trace back to the portion of the image that contains the handwritten text starting from the transcription can be essential for the study of the document by sc...

Despite recent advances in automatic text recognition, the performance remains moderate when it comes to historical manuscripts. This is mainly because of the scarcity of available labelled data to train the data-hungry Handwritten Text Recognition (HTR) models. The Keyword Spotting System (KWS) provides a valid alternative to HTR due to the reduct...

Despite recent advances in automatic text recognition, the performance remains moderate when it comes to historical manuscripts. This is mainly because of the scarcity of available labelled data to train the data-hungry Handwritten Text Recognition (HTR) models. The Keyword Spotting System (KWS) provides a valid alternative to HTR due to the reduct...

Neurodegenerative diseases are caused by the progressive degeneration of nerve cells that affect motor skills and cognitive abilities with increasing severity. Unfortunately, there is no cure for this type of disease and their impact can only be slowed down with specific pharmacological and rehabilitative therapies. Early diagnosis, therefore, rema...

We present a method for discriminating between healthy subjects and Alzheimer’s diseases patients from on-line handwriting. Departing from the current state of the art methods, that adopts machine learning methods and tools for building the classifier, we propose to apply the Negative Selection Algorithm. The major advantage of the proposed method...

Nowadays, the treatments of neurodegenerative diseases are increasingly sophisticated, mainly thanks to innovations in the medical field. As the effectiveness of care, strategies is enhanced by the early diagnosis, in recent years there has been an increasing interest in developing reliable, non-invasive, easy to administer, and cheap diagnostics t...

This paper proposes a performance model for estimating the user time needed to transcribe small collections of handwritten documents using a keyword spotting system (KWS) that provides a number of possible transcriptions for each word image. The model assumes that only information obtained from a small training set is available, and establishes the...