Semi-automatic Segmentation & Alignment of Handwritten



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Acknowledgements


First of all, I would like to give a big thank you to my supervisor Anders Hast for his support and guidance throughout the project. I would also like to thank my subject reader Ingela Nyström for all her feedback and for being available for questions. I would like to thank my examiner Siv Andersson and the course coordinator Lena Henriksson for their support and guidance during the project. Lastly, I would like to thank the student opponent Linde Brokmar for his valuable feedback.


Some computations were enabled by resources provided by the National Academic In- frastructure for Supercomputing in Sweden (NAISS) at Alvis, C3SE partially funded by the Swedish Research Council through grant agreement no. 2022-06725.

References


AOrOc-laboratory. 2023. escriptorium. accessed 2023-01-31 at: https://www. escriptorium.uk/.


Capobianco S, Scommegna L, Marinai S. 2018. Historical handwritten document seg- mentation by using a weighted loss. Pancioni L, Schwenker F, Trentin E, editors, Artificial Neural Networks in Pattern Recognition. Springer International Publishing, Cham, 395–406.
Chavan A. 2021. opencvdragrect, commit=d56ac9c4ab50ff46ecbac35fd2784f042f3014f1.
https://github.com/arccoder/opencvdragrect.
library of Congress T. 2016. Technical metadata for layout and text objects, accessed 2023-03-21 at: https://www.loc.gov/standards/alto/.
De Gregorio G, Capriolo G, Marcelli A. 2023. End-to-end transcript alignment of 17th century manuscripts: The case of moccia code. Journal of Imaging 9.
De Gregorio G, Citro I, Marcelli A. 2022. Transcript alignment for historical handwritten documents: The mim algorithm. Carmona-Duarte C, Diaz M, Ferrer MA, Morales A, editors, Intertwining Graphonomics with Human Movements. Springer International Publishing, Cham, 45–60.
Gonzalez RC, Woods RE. 2008. Digital image fundamentals. Digital Image Processing, Third Edition. Pearson Education International, 90–93.
Isaac A. 2020. Evaluation of word segmentation algorithms applied on handwritten text.
DiVA Portal .
Kumar H. 2020. Word segmentation, commit=dd03f5c4832db2e1c3e01de4b8b262cd3837dc17.
https://github.com/harshavkumar/word_segmentation.
Luff M, Davies I. 2023. Anvil. accessed 2023-01-31 at: https://anvil.works/.
Marti UV, Bunke H. 2002. The iam-database: An english sentence database for offline handwriting recognition. International Journal on Document Analysis and Recogni- tion 5: 39–46.
McKeen K. 2021. Preprocessinghtr, commit=a7926c5c497ea565cf4f21c356e4e2a880e6bf82.
https://github.com/KadenMc/PreprocessingHTR.
Mitchell TM. 1997. Machine learning, volume 1. McGraw-hill New York.

Neto AFdS, Bezerra BLD, Toselli AH. 2020. Towards the natural language processing as spelling correction for offline handwritten text recognition systems. Applied Sciences 10.


READ-COOP. 2023. Transkribus. accessed 2023-01-31 at: https://readcoop.eu/ transkribus/.
Romero-Gómez V, Toselli AH, Bosch V, Sánchez JA, Vidal E. 2018. Automatic align- ment of handwritten images and transcripts for training handwritten text recognition systems. 2018 13th IAPR International Workshop on Document Analysis Systems (DAS). 328–333.
Roy-Rosenzweig-Center. 2008. Omeka. accessed 2023-05-23 at: https://omeka. org/.
Ryu J, Koo HI, Cho NI. 2015. Word segmentation method for handwritten documents based on structured learning. IEEE Signal Processing Letters 22: 1161–1165.
Souibgui MA, Bensalah A, Chen J, Fornés A, Waldispühl M. 2022. A user perspective on htr methods for the automatic transcription of rare scripts: The case of codex runicus.
J. Comput. Cult. Herit. Just Accepted.
Vats E, Hast A. 2017. On-the-fly historical handwritten text annotation. 2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR) 08: 10– 14.
van der Veen T. 2023. Unique software to transcribe historical texts now open source available.
Wilkinson T, Brun A. 2015. A novel word segmentation method based on object de- tection and deep learning. Bebis G, Boyle R, Parvin B, Koracin D, Pavlidis I, Feris R, McGraw T, Elendt M, Kopper R, Ragan E, Ye Z, Weber G, editors, Advances in Visual Computing. Springer International Publishing, Cham, 231–240.

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Supplementary files


Raw data used in the performance experiments can be found HERE.


The GitHub project can be found at the repository: Text_alignment_and_segmentation




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