most citedPreserving Privacy Without Compromising Accuracy: Machine Unlearning for Handwritten Text Recognition

3 citations · 3 across the 6 of their papers we have counts for

collaborators

6 papers

cs.CV2026

Unsupervised Domain Adaptation for Symbol Spotting in Historical Encrypted Manuscripts

Giuseppe De Gregorio, Alicia Fornés, Lei Kang +1

The decipherment of historical encrypted manuscripts poses a fundamental challenge in Digital Humanities: before any transcription can begin, the symbol inventory of the underlying…

cs.CV2026

Joint Transcription and Decryption of Images of Encrypted Handwritten Documents: A Comparison with the Traditional Pipeline

Marino Oliveros-Blanco, Lei Kang, Alicia Fornés +1

Historical encrypted manuscripts present a challenging problem at the intersection of cryptology, linguistics, paleography, and computer vision. Current automatic decipherment appr…

cs.CV2026

A Dataset for the Recognition of Historical and Handwritten Music Scores in Western Notation

Pau Torras, Jiří Mayer, Carles Badal +7

A large amount of musical heritage has been digitised by memory institutions: libraries, museums, and archives. Nevertheless, the field of Optical Music Recognition (OMR) has strug…

cs.CV2026

Learning to Decipher from Pixels: A Case Study of Copiale

Lei Kang, Giuseppe De Gregorio, Raphaela Heil +2

Historical encrypted manuscripts require both paleographic interpretation of cipher symbols and cryptanalytic recovery of plaintext. Most existing computational workflows rely on a…

cs.CV2025

GAN-based Content-Conditioned Generation of Handwritten Musical Symbols

Gerard Asbert, Pau Torras, Lei Kang +2

The field of Optical Music Recognition (OMR) is currently hindered by the scarcity of real annotated data, particularly when dealing with handwritten historical musical scores. In…

cs.CV2025★ 3 cited

Preserving Privacy Without Compromising Accuracy: Machine Unlearning for Handwritten Text Recognition

Lei Kang, Xuanshuo Fu, Lluis Gomez +3

Handwritten Text Recognition (HTR) is crucial for document digitization, but handwritten data can contain user-identifiable features, like unique writing styles, posing privacy ris…