01997nas a2200325   4500000000100000008004100001653002200042653003300064653003400097653002200131653002300153653001800176653005100194653001700245653002600262653003500288653002400323653002100347653000800368653003200376653004000408653002200448653001700470100001400487700001500501245011000516856015600626300001200782520087700794                                       d10aComic recognition10aconvolutional neural network10aConvolutional neural networks10acultural heritage10aCultural heritages10aDeep learning10aFast region-based convolutional neural network10aFaster R-CNN10aHistoric preservation10aIndigenous intangible heritage10aLanguage processing10aLearning systems10aNLP10aNatural language processing10aNatural language processing systems10aNatural languages10aRegion-based1 aR. Sharma1 aV. Kukreja00aDeep learning-Based Comic Recognition and Analysis for the Preservation of Indigenous Intangible Heritage  uhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85190548591&doi=10.1109%2fICSH57060.2023.10482818&partnerID=40&md5=8d63b01aa7c90fc15c23505fd998a70d  a136-1403 aIndigenous intangible heritage is a valuable part of human culture, passed down through generations. This research explores the use of deep learning techniques to preserve and understand this heritage through comics. By detecting and analyzing important elements like speech bubbles and characters, we aim to uncover the cultural significance of these comics in preserving indigenous heritage. Our evaluation of a deep learning pipeline, using Faster Region-based Convolutional Neural Network (R-CNN) and Bidirectional Encoder Representations from Transformers (BERT) models, demonstrates its accuracy and effectiveness in interpreting indigenous intangible heritage through comics. This research sheds light on the potential of deep learning for preserving and promoting indigenous culture, providing valuable insights for researchers and cultural heritage professionals.