{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T05:12:24Z","timestamp":1772082744552,"version":"3.50.1"},"reference-count":28,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2021,12,2]],"date-time":"2021-12-02T00:00:00Z","timestamp":1638403200000},"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>Offline handwritten text recognition (HTR) for historical documents aims for effective transcription by addressing challenges that originate from the low quality of manuscripts under study as well as from several particularities which are related to the historical period of writing. In this paper, the challenge in HTR is related to a focused goal of the transcription of Greek historical manuscripts that contain several particularities. To this end, in this paper, a convolutional recurrent neural network architecture is proposed that comprises octave convolution and recurrent units which use effective gated mechanisms. The proposed architecture has been evaluated on three newly created collections from Greek historical handwritten documents that will be made publicly available for research purposes as well as on standard datasets like IAM and RIMES. For evaluation we perform a concise study which shows that compared to state of the art architectures, the proposed one deals effectively with the challenging Greek historical manuscripts.<\/jats:p>","DOI":"10.3390\/jimaging7120260","type":"journal-article","created":{"date-parts":[[2021,12,2]],"date-time":"2021-12-02T21:19:08Z","timestamp":1638479948000},"page":"260","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["HTR for Greek Historical Handwritten Documents"],"prefix":"10.3390","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4634-7419","authenticated-orcid":false,"given":"Lazaros","family":"Tsochatzidis","sequence":"first","affiliation":[{"name":"Visual Computing Group, Department of Electrical and Computer Engineering, Democritus University of Thrace, 67100 Xanthi, Greece"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3259-614X","authenticated-orcid":false,"given":"Symeon","family":"Symeonidis","sequence":"additional","affiliation":[{"name":"Visual Computing Group, Department of Electrical and Computer Engineering, Democritus University of Thrace, 67100 Xanthi, Greece"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9788-6391","authenticated-orcid":false,"given":"Alexandros","family":"Papazoglou","sequence":"additional","affiliation":[{"name":"Visual Computing Group, Department of Electrical and Computer Engineering, Democritus University of Thrace, 67100 Xanthi, Greece"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4124-3688","authenticated-orcid":false,"given":"Ioannis","family":"Pratikakis","sequence":"additional","affiliation":[{"name":"Visual Computing Group, Department of Electrical and Computer Engineering, Democritus University of Thrace, 67100 Xanthi, Greece"}]}],"member":"1968","published-online":{"date-parts":[[2021,12,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"De Sousa Neto, A.F., Bezerra, B.L.D., Toselli, A.H., and Lima, E.B. 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