The extraction of relevant information carried out by named entities in handwriting documents is still a challenging task. Unlike traditional information extraction approaches …
Handwritten text recognition in low resource scenarios, such as manuscripts with rare alphabets, is a challenging problem. In this paper, we propose a few-shot learning-based …
SK Jemni, S Ammar, Y Kessentini - Neural Computing and Applications, 2022 - Springer
Abstract Arabic Handwritten Text Recognition (AHTR) based on deep learning approaches remains a challenging problem due to the inevitable domain shift like the variability among …
Abstract Low resource Handwritten Text Recognition (HTR) is a hard problem due to the scarce annotated data and the very limited linguistic information (dictionaries and language …
G De Gregorio, S Biswas, MA Souibgui… - … Conference on Frontiers …, 2022 - Springer
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 …
We present a generative document-specific approach to character analysis and recognition in text lines. Our main idea is to build on unsupervised multi-object segmentation methods …
Recent breakthroughs in Artificial Intelligence, Deep Learning, and Document Image Analysis and Recognition have significantly eased the creation of digital libraries and the …
Optical character recognition (OCR) has proved a powerful tool for the digital analysis of printed historical documents. However, its ability to localize and identify individual glyphs is …
Historical records are invaluable sources of information that provide insights into multiple aspects of past events and societies. The analysis of historical records using deep learning …