Hyperspectral imaging and convolutional neural networks for augmented documentation of ancient Egyptian artefacts
Abstract The study aims at investigating the use of reflectance Hyperspectral Imaging (HSI) in the Visible (Vis) and Near Infrared (NIR) range in combination with Deep Convolutional Neural Networks (CNN) to address the tasks related to ancient Egyptian hieroglyphs recognition. Recently, well-establi...
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2024-03-01
|
| Series: | Heritage Science |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s40494-024-01182-9 |
| _version_ | 1797273569685667840 |
|---|---|
| author | Costanza Cucci Tommaso Guidi Marcello Picollo Lorenzo Stefani Lorenzo Python Fabrizio Argenti Andrea Barucci |
| author_facet | Costanza Cucci Tommaso Guidi Marcello Picollo Lorenzo Stefani Lorenzo Python Fabrizio Argenti Andrea Barucci |
| author_sort | Costanza Cucci |
| collection | DOAJ |
| description | Abstract The study aims at investigating the use of reflectance Hyperspectral Imaging (HSI) in the Visible (Vis) and Near Infrared (NIR) range in combination with Deep Convolutional Neural Networks (CNN) to address the tasks related to ancient Egyptian hieroglyphs recognition. Recently, well-established CNN architectures trained to address segmentation of objects within images have been successfully tested also for trial sets of hieroglyphs. In real conditions, however, the surfaces of the artefacts can be highly degraded, featuring corrupted and scarcely readable inscriptions which highly reduce the CNNs capabilities in automated recognition of symbols. In this study, the use of HSI technique in the extended Vis-NIR range is proposed to retrieve readability of degraded symbols by exploiting spectral images. Using different algorithmic chains, HSI data are processed to obtain enhanced images to be fed to the CNN architectures. In this pilot study, an ancient Egyptian coffin (XXV Dynasty), featuring a degraded hieroglyphic inscription, was used as a benchmark to test, in real conditions, the proposed methodological approaches. A set of Vis-NIR HSI data acquired on-site, in the framework of a non-invasive diagnostic campaign, was used in combination with CNN architectures to perform hieroglyphs segmentation. The outcomes of the different methodological approaches are presented and compared to each other and to the results obtained using standard RGB images. |
| first_indexed | 2024-03-07T14:46:16Z |
| format | Article |
| id | doaj.art-00c8394f01de4ace87d1baa7799aac75 |
| institution | Directory Open Access Journal |
| issn | 2050-7445 |
| language | English |
| last_indexed | 2024-03-07T14:46:16Z |
| publishDate | 2024-03-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Heritage Science |
| spelling | doaj.art-00c8394f01de4ace87d1baa7799aac752024-03-05T19:55:36ZengSpringerOpenHeritage Science2050-74452024-03-0112111510.1186/s40494-024-01182-9Hyperspectral imaging and convolutional neural networks for augmented documentation of ancient Egyptian artefactsCostanza Cucci0Tommaso Guidi1Marcello Picollo2Lorenzo Stefani3Lorenzo Python4Fabrizio Argenti5Andrea Barucci6Institute of Applied Physics, “Nello Carrara”, IFAC-CNRInstitute of Applied Physics, “Nello Carrara”, IFAC-CNRInstitute of Applied Physics, “Nello Carrara”, IFAC-CNRInstitute of Applied Physics, “Nello Carrara”, IFAC-CNRDepartment of Information Engineering, University of FlorenceDepartment of Information Engineering, University of FlorenceInstitute of Applied Physics, “Nello Carrara”, IFAC-CNRAbstract The study aims at investigating the use of reflectance Hyperspectral Imaging (HSI) in the Visible (Vis) and Near Infrared (NIR) range in combination with Deep Convolutional Neural Networks (CNN) to address the tasks related to ancient Egyptian hieroglyphs recognition. Recently, well-established CNN architectures trained to address segmentation of objects within images have been successfully tested also for trial sets of hieroglyphs. In real conditions, however, the surfaces of the artefacts can be highly degraded, featuring corrupted and scarcely readable inscriptions which highly reduce the CNNs capabilities in automated recognition of symbols. In this study, the use of HSI technique in the extended Vis-NIR range is proposed to retrieve readability of degraded symbols by exploiting spectral images. Using different algorithmic chains, HSI data are processed to obtain enhanced images to be fed to the CNN architectures. In this pilot study, an ancient Egyptian coffin (XXV Dynasty), featuring a degraded hieroglyphic inscription, was used as a benchmark to test, in real conditions, the proposed methodological approaches. A set of Vis-NIR HSI data acquired on-site, in the framework of a non-invasive diagnostic campaign, was used in combination with CNN architectures to perform hieroglyphs segmentation. The outcomes of the different methodological approaches are presented and compared to each other and to the results obtained using standard RGB images.https://doi.org/10.1186/s40494-024-01182-9Vis-NIR reflectance hyperspectral imagingConvolutional neural networksAncient Egyptian hieroglyphsSegmentationText recognition |
| spellingShingle | Costanza Cucci Tommaso Guidi Marcello Picollo Lorenzo Stefani Lorenzo Python Fabrizio Argenti Andrea Barucci Hyperspectral imaging and convolutional neural networks for augmented documentation of ancient Egyptian artefacts Heritage Science Vis-NIR reflectance hyperspectral imaging Convolutional neural networks Ancient Egyptian hieroglyphs Segmentation Text recognition |
| title | Hyperspectral imaging and convolutional neural networks for augmented documentation of ancient Egyptian artefacts |
| title_full | Hyperspectral imaging and convolutional neural networks for augmented documentation of ancient Egyptian artefacts |
| title_fullStr | Hyperspectral imaging and convolutional neural networks for augmented documentation of ancient Egyptian artefacts |
| title_full_unstemmed | Hyperspectral imaging and convolutional neural networks for augmented documentation of ancient Egyptian artefacts |
| title_short | Hyperspectral imaging and convolutional neural networks for augmented documentation of ancient Egyptian artefacts |
| title_sort | hyperspectral imaging and convolutional neural networks for augmented documentation of ancient egyptian artefacts |
| topic | Vis-NIR reflectance hyperspectral imaging Convolutional neural networks Ancient Egyptian hieroglyphs Segmentation Text recognition |
| url | https://doi.org/10.1186/s40494-024-01182-9 |
| work_keys_str_mv | AT costanzacucci hyperspectralimagingandconvolutionalneuralnetworksforaugmenteddocumentationofancientegyptianartefacts AT tommasoguidi hyperspectralimagingandconvolutionalneuralnetworksforaugmenteddocumentationofancientegyptianartefacts AT marcellopicollo hyperspectralimagingandconvolutionalneuralnetworksforaugmenteddocumentationofancientegyptianartefacts AT lorenzostefani hyperspectralimagingandconvolutionalneuralnetworksforaugmenteddocumentationofancientegyptianartefacts AT lorenzopython hyperspectralimagingandconvolutionalneuralnetworksforaugmenteddocumentationofancientegyptianartefacts AT fabrizioargenti hyperspectralimagingandconvolutionalneuralnetworksforaugmenteddocumentationofancientegyptianartefacts AT andreabarucci hyperspectralimagingandconvolutionalneuralnetworksforaugmenteddocumentationofancientegyptianartefacts |