{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,22]],"date-time":"2026-01-22T06:03:17Z","timestamp":1769061797232,"version":"3.49.0"},"reference-count":37,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2020,9,4]],"date-time":"2020-09-04T00:00:00Z","timestamp":1599177600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>In the framework of palaeography, the availability of both effective image analysis algorithms, and high-quality digital images has favored the development of new applications for the study of ancient manuscripts and has provided new tools for decision-making support systems. The quality of the results provided by such applications, however, is strongly influenced by the selection of effective features, which should be able to capture the distinctive aspects to which the paleography expert is interested in. This process is very difficult to generalize due to the enormous variability in the type of ancient documents, produced in different historical periods with different languages and styles. The effect is that it is very difficult to define standard techniques that are general enough to be effectively used in any case, and this is the reason why ad-hoc systems, generally designed according to paleographers\u2019 suggestions, have been designed for the analysis of ancient manuscripts. In recent years, there has been a growing scientific interest in the use of techniques based on deep learning (DL) for the automatic processing of ancient documents. This interest is not only due to their capability of designing high-performance pattern recognition systems, but also to their ability of automatically extracting features from raw data, without using any a priori knowledge. Moving from these considerations, the aim of this study is to verify if DL-based approaches may actually represent a general methodology for automatically designing machine learning systems for palaeography applications. To this purpose, we compared the performance of a DL-based approach with that of a \u201cclassical\u201d machine learning one, in a particularly unfavorable case for DL, namely that of highly standardized schools. The rationale of this choice is to compare the obtainable results even when context information is present and discriminating: this information is ignored by DL approaches, while it is used by machine learning methods, making the comparison more significant. The experimental results refer to the use of a large sets of digital images extracted from an entire 12th-century Bibles, the \u201cAvila Bible\u201d. This manuscript, produced by several scribes who worked in different periods and in different places, represents a severe test bed to evaluate the efficiency of scribe identification systems.<\/jats:p>","DOI":"10.3390\/jimaging6090089","type":"journal-article","created":{"date-parts":[[2020,9,4]],"date-time":"2020-09-04T11:24:24Z","timestamp":1599218664000},"page":"89","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["An Experimental Comparison between Deep Learning and Classical Machine Learning Approaches for Writer Identification in Medieval Documents"],"prefix":"10.3390","volume":"6","author":[{"given":"Nicole Dalia","family":"Cilia","sequence":"first","affiliation":[{"name":"Department of Electrical and Information Engineering \u201cMaurizio Scarano\u201d, University of Cassino and Southern Lazio, 03043 Cassino (FR), Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7654-6849","authenticated-orcid":false,"given":"Claudio","family":"De Stefano","sequence":"additional","affiliation":[{"name":"Department of Electrical and Information Engineering \u201cMaurizio Scarano\u201d, University of Cassino and Southern Lazio, 03043 Cassino (FR), Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3242-0179","authenticated-orcid":false,"given":"Francesco","family":"Fontanella","sequence":"additional","affiliation":[{"name":"Department of Electrical and Information Engineering \u201cMaurizio Scarano\u201d, University of Cassino and Southern Lazio, 03043 Cassino (FR), Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0840-7350","authenticated-orcid":false,"given":"Claudio","family":"Marrocco","sequence":"additional","affiliation":[{"name":"Department of Electrical and Information Engineering \u201cMaurizio Scarano\u201d, University of Cassino and Southern Lazio, 03043 Cassino (FR), Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6144-0654","authenticated-orcid":false,"given":"Mario","family":"Molinara","sequence":"additional","affiliation":[{"name":"Department of Electrical and Information Engineering \u201cMaurizio Scarano\u201d, University of Cassino and Southern Lazio, 03043 Cassino (FR), Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9642-3555","authenticated-orcid":false,"given":"Alessandra Scotto di","family":"Freca","sequence":"additional","affiliation":[{"name":"Department of Electrical and Information Engineering \u201cMaurizio Scarano\u201d, University of Cassino and Southern Lazio, 03043 Cassino (FR), Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,9,4]]},"reference":[{"key":"ref_1","unstructured":"Stokes, P. (2009). Computer-Aided Palaeography, Present and Future. Kodikologie und Pal\u00e4ographie im Digitalen Zeitalter\u2014Codicology and Palaeography in the Digital Age, Institut f\u00fcr Dokumentologie und Editorik."},{"key":"ref_2","unstructured":"Rehbein, M., Sahle, P., and Scha\u00dfan, T. (2009). The Palaeographical Method Under the Light of a Digital Approach. Kodikologie und Pal\u00e4ographie im digitalen Zeitalter-Codicology and Palaeography in the Digital Age, Institut f\u00fcr Dokumentologie und Editorik."},{"key":"ref_3","unstructured":"Rehbein, M., Sahle, P., and Scha\u00dfan, T. (2009). \u201cGraphoskop\u201d, uno Strumento Informatico per l\u2019analisi Ialeografica Quantitativa. Kodikologie und Pal\u00e4ographie im digitalen Zeitalter-Codicology and Palaeography in the Digital Age, Institut f\u00fcr Dokumentologie und Editorik."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"393","DOI":"10.1007\/978-3-642-24085-0_41","article-title":"A Method for Scribe Distinction in Medieval Manuscripts Using Page Layout Features","volume":"Volume 6978","author":"Maino","year":"2011","journal-title":"Image Analysis and Processing\u2014ICIAP 2011"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1016\/j.engappai.2018.03.023","article-title":"Reliable writer identification in medieval manuscripts through page layout features: The Avila Bible case","volume":"72","author":"Maniaci","year":"2018","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1016\/j.cviu.2014.01.003","article-title":"Identifying the writer of ancient inscriptions and Byzantine codices. A novel approach","volume":"121","author":"Papaodysseus","year":"2014","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Wahlberg, F., M\u00e5rtensson, L., and Brun, A. (2015). Large Scale Style Based Dating of Medieval Manuscripts. HIP \u201915: Proceedings of the 3rd International Workshop on Historical Document Imaging and Processing, ACM.","DOI":"10.1145\/2809544.2809560"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Pintus, R., Yang, Y., Gobbetti, E., and Rushmeier, H. (October, January 28). An automatic word-spotting framework for medieval manuscripts. Proceedings of the 2015 Digital Heritage, Granada, Spain.","DOI":"10.1109\/DigitalHeritage.2015.7419446"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1016\/j.patcog.2016.01.014","article-title":"A scalable pattern spotting system for historical documents","volume":"54","author":"En","year":"2016","journal-title":"Pattern Recognit."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"701","DOI":"10.1109\/TPAMI.2007.1009","article-title":"Text-Independent Writer Identification and Verification Using Textural and Allographic Features","volume":"29","author":"Bulacu","year":"2007","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Dhali, M.A., He, S., Popovic, M., Tigchelaar, E., and Schomaker, L. (2017, January 24\u201326). A Digital Palaeographic Approach towards Writer Identification in the Dead Sea Scrolls. Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods, ICPRAM, Porto, Portugal.","DOI":"10.5220\/0006249706930702"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1653001","DOI":"10.1142\/S0218001416530013","article-title":"Automatic Handwriting Feature Extraction, Analysis and Visualization in the Context of Digital Palaeography","volume":"30","author":"Liang","year":"2016","journal-title":"Int. J. Pattern Recognit. Artif. Intell."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1016\/j.patcog.2016.03.032","article-title":"Image-based historical manuscript dating using contour and stroke fragments","volume":"58","author":"He","year":"2016","journal-title":"Pattern Recognit."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"105778","DOI":"10.1016\/j.asoc.2019.105778","article-title":"Electrocardiogram soft computing using hybrid deep learning CNN-ELM","volume":"86","author":"Zhou","year":"2020","journal-title":"Appl. Soft Comput."},{"key":"ref_15","first-page":"321","article-title":"Parameters Compressing in Deep Learning","volume":"62","author":"He","year":"2020","journal-title":"Comput. Mater. Contin."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Gadekallu, T.R., Rajput, D.S., Reddy, M.P.K., Lakshmanna, K., Bhattacharya, S., Singh, S., Jolfaei, A., and Alazab, M. (2020). A novel PCA\u2013whale optimization-based deep neural network model for classification of tomato plant diseases using GPU. J. Real Time Image Process., 1\u201314.","DOI":"10.1007\/s11554-020-00987-8"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Savita, A., Choudhary, A., Nayyar, A., Singh, S., and Yoon, B. (2020). Improved Handwritten Digit Recognition Using Convolutional Neural Networks (CNN). Sensors, 20.","DOI":"10.3390\/s20123344"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"7435","DOI":"10.1007\/s10586-018-1772-4","article-title":"A novel online incremental and decremental learning algorithm based on variable support vector machine","volume":"22","author":"Chen","year":"2018","journal-title":"Clust. Comput."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Ly, N.T., Nguyen, C.T., and Nakagawa, M. (2020). An attention-based row-column encoder-decoder model for text recognition in Japanese Historical Documents. Pattern Recognit. Lett.","DOI":"10.1016\/j.patrec.2020.05.026"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1016\/j.patrec.2020.02.015","article-title":"Nom document digitalization by deep convolution neural networks","volume":"133","author":"Nguyen","year":"2020","journal-title":"Pattern Recognit. Lett."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1016\/j.patrec.2020.02.016","article-title":"Text alignment in early printed books combining deep learning and dynamic programming","volume":"133","author":"Ziran","year":"2020","journal-title":"Pattern Recognit. Lett."},{"key":"ref_22","unstructured":"Bozzolo, C., Coq, D., Muzerelle, D., and Ornato, E. (1982). Noir et Blanc. Premiers R\u00e9sultats d\u2019une Enqu\u00eate sur la Mise en Page dans le Livre M\u00e9di\u00e9val, Universit\u00e0 degli Studi di Urbino. Il Libro e il Testo."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., and Chen, L.C. (2018, January 18\u201322). MobileNetV2: Inverted Residuals and Linear Bottlenecks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00474"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.Y., and Berg, A.C. (2016). Ssd: Single Shot Multibox Detector. Computer Vision\u2014ECCV 2016, Proceedings of the European Conference on Computer Vision, Amsterdam, The Netherlands, 8\u201316 October 2016, Springer.","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"ref_25","unstructured":"Quinlan, J.R. (1993). C4.5: Programs for Machine Learning (Morgan Kaufmann Series in Machine Learning), Morgan Kaufmann."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random Forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"533","DOI":"10.1038\/323533a0","article-title":"Learning representations by back-propagating errors","volume":"323","author":"Rumelhart","year":"1986","journal-title":"Nature"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1016\/j.patrec.2019.11.025","article-title":"An end-to-end deep learning system for medieval writer identification","volume":"129","author":"Cilia","year":"2020","journal-title":"Pattern Recognit. Lett."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Fleet, D., Pajdla, T., Schiele, B., and Tuytelaars, T. (2014). Microsoft COCO: Common Objects in Context. Computer Vision\u2014ECCV 2014, Springer International Publishing.","DOI":"10.1007\/978-3-319-10590-1"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Ioffe, S., Vanhoucke, V., and Alemi, A.A. (2017, January 4\u20139). Inception-V4, Inception-ResNet and the impact of residual connections on learning. Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, San Francisco, CA, USA.","DOI":"10.1609\/aaai.v31i1.11231"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z. (2016, January 27\u201330). Rethinking the inception architecture for computer vision. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.308"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Zoph, B., Vasudevan, V., Shlens, J., and Le, Q.V. (2018, January 18\u201322). Learning transferable architectures for scalable image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00907"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_34","unstructured":"Simonyan, K., and Zisserman, A. (2014). Very Deep Convolutional Networks for Large-Scale Image Recognition. arXiv."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., and Li, F.F. (2009, January 20\u201325). ImageNet: A large-scale hierarchical image database. Proceedings of the CVPR IEEE Computer Society, Miami, FL, USA.","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"198","DOI":"10.1016\/j.patrec.2019.11.030","article-title":"What is the minimum training data size to reliably identify writers in medieval manuscripts?","volume":"129","author":"Cilia","year":"2020","journal-title":"Pattern Recognit. Lett."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1109\/TIT.1970.1054406","article-title":"On optimum recognition error and reject trade off","volume":"16","author":"Chow","year":"2006","journal-title":"IEEE Trans. Inf. Theor."}],"container-title":["Journal of Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2313-433X\/6\/9\/89\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:06:51Z","timestamp":1760177211000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2313-433X\/6\/9\/89"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,9,4]]},"references-count":37,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2020,9]]}},"alternative-id":["jimaging6090089"],"URL":"https:\/\/doi.org\/10.3390\/jimaging6090089","relation":{},"ISSN":["2313-433X"],"issn-type":[{"value":"2313-433X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,9,4]]}}}