Abstract
Performing Named Entity Recognition on Handwritten Documents results in categorizing particular fragments of the automatic transcription which may be employed in information extraction processes. Different corpora employ different tagging notations to identify Named Entities, which may affect the performance of the trained model. In this work, we analyze three different tagging notations on three databases of handwritten line-level images. During the experimentation, we train the same Convolutional Recurrent Neural Network (CRNN) and n-gram character Language Model on the resulting data and observe how choosing the best tagging notation depending on the characteristics of each task leads to noticeable performance increments.
This work was supported by Grant PID2020-116813RB-I00 funded by MCIN/AEI/10.13039/501100011033, by Grant ACIF/2021/436 funded by Generalitat Valenciana and by Grant PID2021-124719OB-I00 funded by MCIN/AEI/10.13039/501100011033 and by ERDF, EU A way of making Europe.
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Notes
- 1.
The documentation for the employed Simplex implementation is available at: https://docs.scipy.org/doc/scipy/reference/optimize.linprog-simplex.html.
References
Abadie, N., Carlinet, E., Chazalon, J., Duménieu, B.: A benchmark of named entity recognition approaches in historical documents application to 19th century French directories. In: Uchida, S., Barney, E., Eglin, V. (eds.) Document Analysis Systems, pp. 445–460. Springer International Publishing, Cham (2022). https://doi.org/10.1007/978-3-031-06555-2_30
Bluche, T.: Deep Neural Networks for Large Vocabulary Handwritten Text Recognition. Ph.D. thesis, Université Paris Sud-Paris XI (2015)
Boroş, E., et al.: A comparison of sequential and combined approaches for named entity recognition in a corpus of handwritten medieval charters. In: 2020 17th International Conference on Frontiers in Handwriting Recognition (ICFHR), pp. 79–84. IEEE (2020)
Carbonell, M., Villegas, M., Fornés, A., Lladós, J.: Joint recognition of handwritten text and named entities with a neural end-to-end model. In: 2018 13th IAPR International Workshop on Document Analysis Systems (DAS), pp. 399–404. IEEE (2018)
Catelli, R., Casola, V., De Pietro, G., Fujita, H., Esposito, M.: Combining contextualized word representation and sub-document level analysis through bi-LSTM+ CRF architecture for clinical de-identification. Knowl.-Based Syst. 213, 106649 (2021)
Fischer, A., Keller, A., Frinken, V., Bunke, H.: Lexicon-free handwritten word spotting using character HMMS. Pattern Recognition Letters 33(7), 934–942 (2012). https://doi.org/10.1016/j.patrec.2011.09.009, special Issue on Awards from ICPR 2010
Johansson, S., Leech, G., Goodluck, H.: Manual of information to accompany the lancaster-oslo-bergen corpus of British English, for use with digital computers (1978). http://korpus.uib.no/icame/manuals/LOB/INDEX.HTM
Kang, L., Toledo, J.I., Riba, P., Villegas, M., Fornés, A., Rusiñol, M.: Convolve, attend and spell: an attention-based sequence-to-sequence model for handwritten word recognition. In: Brox, T., Bruhn, A., Fritz, M. (eds.) GCPR 2018. LNCS, vol. 11269, pp. 459–472. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-12939-2_32
Lafferty, J.D., McCallum, A., Pereira, F.C.N.: Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In: Proceedings of the Eighteenth International Conference on Machine Learning, ICML 2001, pp. 282–289. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (2001)
Maarand, M., Beyer, Y., Kåsen, A., Fosseide, K.T., Kermorvant, C.: A comprehensive comparison of open-source libraries for handwritten text recognition in norwegian. In: Uchida, S., Barney, E., Eglin, V. (eds.) Document Analysis Systems, pp. 399–413. Springer International Publishing, Cham (2022). https://doi.org/10.1007/978-3-031-06555-2_27
Marti, U.V., Bunke, H.: The i am-database: an English sentence database for offline handwriting recognition. Int. J. Doc. Anal. Recogn. 5(1), 39–46 (2002)
Mocholí Calvo, C.: Development and experimentation of a deep learning system for convolutional and recurrent neural networks. Degree’s thesis, Universitat Politècnica de València (2018)
Mohit, B.: Named entity recognition. In: Zitouni, I. (ed.) Natural Language Processing of Semitic Languages. TANLP, pp. 221–245. Springer, Heidelberg (2014). https://doi.org/10.1007/978-3-642-45358-8_7
Monroc, C.B., Miret, B., Bonhomme, M.L., Kermorvant, C.: A comprehensive study of open-source libraries for named entity recognition on handwritten historical documents. In: Uchida, S., Barney, E., Eglin, V. (eds.) Document Analysis Systems, pp. 429–444. Springer International Publishing, Cham (2022). https://doi.org/10.1007/978-3-031-06555-2_29
Povey, D., et al.: The kaldi speech recognition toolkit. In: IEEE 2011 Workshop on Automatic Speech Recognition and Understanding. No. CFP11SRW-USB, IEEE Signal Processing Society (2011)
Puigcerver, J.: Are multidimensional recurrent layers really necessary for handwritten text recognition? In: 2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR), vol. 1, pp. 67–72. IEEE (2017)
Romero, V., et al.: The Esposalles database: an ancient marriage license corpus for off-line handwriting recognition. Pattern Recognit. 46(6), 1658–1669 (2013). https://doi.org/10.1016/j.patcog.2012.11.024
Rowtula, V., Krishnan, P., Jawahar, C.: Pos tagging and named entity recognition on handwritten documents. In: Proceedings of the 15th International Conference on Natural Language Processing, p. 87–91 (2018)
Sánchez, J.A., Bosch, V., Romero, V., Depuydt, K., De Does, J.: Handwritten text recognition for historical documents in the transcriptorium project. In: Proceedings of the First International Conference on Digital Access to Textual Cultural Heritage, pp. 111–117 (2014)
Stolcke, A.: Srilm - an extensible language modeling toolkit. In: Proceedings of 7th International Conference on Spoken Language Processing (ICSLP 2002), pp. 901–904 (2002)
Tarride, S., Lemaitre, A., Coéasnon, B., Tardivel, S.: A comparative study of information extraction strategies using an attention-based neural network. In: Uchida, S., Barney, E., Eglin, V. (eds.) Document Analysis Systems. DAS 2022. LNCS, vol. 13237, pp. 644–658. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-06555-2_43
Tjong Kim Sang, E.F., Buchholz, S.: Introduction to the CoNLL-2000 shared task chunking. In: Fourth Conference on Computational Natural Language Learning and the Second Learning Language in Logic Workshop, pp. 127–132 (2000). https://aclanthology.org/W00-0726
Tüselmann, O., Fink, G.A.: Named entity linking on handwritten document images. In: Uchida, S., Barney, E., Eglin, V. (eds.) Document Analysis Systems, pp. 199–213. Springer International Publishing, Cham (2022). https://doi.org/10.1007/978-3-031-06555-2_14
Tüselmann, O., Wolf, F., Fink, G.A.: Are end-to-end systems really necessary for NER on handwritten document images? In: Lladós, J., Lopresti, D., Uchida, S. (eds.) ICDAR 2021. LNCS, vol. 12822, pp. 808–822. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-86331-9_52
Villanova-Aparisi, D.: Line-level named entity recognition annotation for the George Washington and IAM datasets (2023). https://doi.org/10.5281/zenodo.7805128
Villanova-Aparisi, D., Martínez-Hinarejos, C.D., Romero, V., Pastor-Gadea, M.: Evaluation of named entity recognition in handwritten documents. In: Uchida, S., Barney, E., Eglin, V. (eds.) Document Analysis Systems. DAS 2022. LNCS, vol. 13237, pp. 568–582. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-06555-2_38
Villegas, M., Romero, V., Sánchez, J.A.: On the modification of binarization algorithms to retain grayscale information for handwritten text recognition. In: Paredes, R., Cardoso, J.S., Pardo, X.M. (eds.) IbPRIA 2015. LNCS, vol. 9117, pp. 208–215. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-19390-8_24
Wen, Y., Fan, C., Chen, G., Chen, X., Chen, M.: A survey on named entity recognition. In: Liang, Q., Wang, W., Liu, X., Na, Z., Jia, M., Zhang, B. (eds.) CSPS 2019. LNEE, vol. 571, pp. 1803–1810. Springer, Singapore (2020). https://doi.org/10.1007/978-981-13-9409-6_218
Wick, C., Zöllner, J., Grüning, T.: Transformer for handwritten text recognition using bidirectional post-decoding. In: Lladós, J., Lopresti, D., Uchida, S. (eds.) ICDAR 2021. LNCS, vol. 12823, pp. 112–126. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-86334-0_8
Xingjian, S., Chen, Z., Wang, H., Yeung, D.Y., Wong, W.K., Woo, W.C.: Convolutional lstm network: a machine learning approach for precipitation nowcasting. In: Advances in Neural Information Processing Systems, pp. 802–810 (2015)
Yadav, V., Bethard, S.: A survey on recent advances in named entity recognition from deep learning models. In: Proceedings of the 27th International Conference on Computational Linguistics. pp. 2145–2158. Association for Computational Linguistics, Santa Fe, New Mexico, USA, August 2018. https://aclanthology.org/C18-1182
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Villanova-Aparisi, D., Martínez-Hinarejos, CD., Romero, V., Pastor-Gadea, M. (2023). Evaluation of Different Tagging Schemes for Named Entity Recognition in Handwritten Documents. In: Fink, G.A., Jain, R., Kise, K., Zanibbi, R. (eds) Document Analysis and Recognition - ICDAR 2023. ICDAR 2023. Lecture Notes in Computer Science, vol 14189. Springer, Cham. https://doi.org/10.1007/978-3-031-41682-8_1
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