Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Bücher
Zeitschriften
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
MTZ-Fachkongress

Antriebe und Energiesysteme von morgen


Austausch von Automobil- und Energiewirtschaft

Am 14./15. Mai geht es in Chemnitz um die Mobilitätswende durch Elektro- und Wasserstofffahrzeuge.
Erweiterte Suche
Anmelden
nach oben
International Journal on Document Analysis and Recognition (IJDAR)
Erschienen in: International Journal on Document Analysis and Recognition (IJDAR) 2/2023

27.10.2022 | Original Paper

Retrieval-based language model adaptation for handwritten Chinese text recognition

verfasst von: Shuying Hu, Qiufeng Wang, Kaizhu Huang, Min Wen, Frans Coenen

Erschienen in: International Journal on Document Analysis and Recognition (IJDAR) | Ausgabe 2/2023

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

In handwritten text recognition, compared to human, computers are far short of linguistic context knowledge, especially domain-matched knowledge. In this paper, we present a novel retrieval-based method to obtain an adaptive language model for offline recognition of unconstrained handwritten Chinese texts. The content of handwritten texts to be recognized is varied and usually unknown a priori. Therefore we adopt a two-pass recognition strategy. In the first pass, we utilize a common language model to obtain initial recognition results, which are used to retrieve the related contents from Internet. In the content retrieval, we evaluate different types of semantic representation from BERT output and the traditional TF–IDF representation. Then, we dynamically generate an adaptive language model from these related contents, which will consequently be combined with the common language model and applied in the second-pass recognition. We evaluate the proposed method on two benchmark unconstrained handwriting datasets, namely CASIA-HWDB and ICDAR-2013. Experimental results show that the proposed retrieval-based language model adaptation yields improvements in recognition performance, despite the reduced Internet contents hereby employed.
Vorheriger Artikel WriterINet: a multi-path deep CNN for offline text-independent writer identification
Nächster Artikel Tables to LaTeX: structure and content extraction from scientific tables

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren
Fußnoten
1
Accordingly, the over-segmentation is also called as explicit segmentation.
 
Literatur
1.
Nagy, G.: Disruptive developments in document recognition. Pattern Recogn. Lett. 79, 106–112 (2016)CrossRef
2.
Fujisawa, H.: Forty years of research in character and document recognition–an industrial perspective. Pattern Recogn. 41(8), 2435–2446 (2008)CrossRef
3.
Dai, R.-W., Liu, C.-L., Xiao, B.-H.: Chinese character recognition: history, status and prospects. Front. Comput. Sci. China 1(2), 126–136 (2007)CrossRef
4.
Liu, C.-L., Lu, Y. (eds.): Advances in Chinese Document and Text Processing, book in Series on Language Processing, Pattern Recognition, and Intelligent Systems, vol. 2. World Scientific (2017)
5.
Liu, C.-L., Yin, F., Wang, Q.-F., Wang, D.-H.: ICDAR 2011 Chinese Handwriting Recognition Competition. Proc. ICDAR, pp.1464–1469 (2011)
6.
Yin, F., Wang, Q.-F., Zhang, X.-Y., Liu, C.-L.: ICDAR 2013 Chinese Handwriting Recognition Competition. Proc. ICDAR, pp. 1464–1470 (2013)
7.
Cheng, C., Zhang, X.Y., Shao, X.H., Zhou, X.D.: Handwritten Chinese Character Recognition by Joint Classification and Similarity Ranking. Proc. Int’l Conf. on Frontiers in Handwriting Recognition (ICFHR), pp. 507-511 (2016)
8.
Zhang, X.-Y., Bengio, Y., Liu, C.-L.: Online and offline handwritten Chinese character recognition: a comprehensive study and new benchmark. Pattern Recogn. 61, 348–360 (2017)CrossRef
9.
Wang, Q.-F., Yin, F., Liu, C.-L.: Handwritten Chinese text recognition by integrating multiple contexts. IEEE Trans. Pattern Anal. Mach. Intell. (PAMI) 34(8), 1469–1481 (2012)CrossRef
10.
Wu, Y.-C., Yin, F., Liu, C.-L.: Improving handwritten Chinese text recognition using neural network language models and convolutional neural network shape models. Pattern Recogn. 65, 251–264 (2017)CrossRef
11.
Wang, Q.-F., Cambria, E., Liu, C.-L., Hussain, A.: Common sense knowledge for handwritten Chinese text recognition. Cogn. Comput. 5(2), 234–242 (2013)CrossRef
12.
Wang, Q.-F., Yin, F., Liu, C.-L.: Unsupervised language model adaptation for handwritten Chinese text recognition. Pattern Recogn. 47(3), 1202–1216 (2014)CrossRef
13.
Li, Y.X., Tan, C.L., Ding, X.Q.: A hybrid postprocessing system for offline handwritten Chinese Script recognition. Pattern Anal. Appl. 8, 272–286 (2005)MathSciNetCrossRef
14.
Xu, R.F., Yeung, D.S., Shi, D.M.: A hybrid postprocessing system for offline handwritten Chinese character recognition based on a statistical language model. Int. J. Pattern Recognit. Artif. Intell. 19(3), 415–428 (2005)CrossRef
15.
Wang, Q.-F., Yin, F., Liu, C.-L.: Integrating language model in handwriting Chinese text recognition. Proc. 10th ICDAR, pp. 1036-1040 (2009)
16.
Carlson, A., Betteridge, J., Kisiel, B., Settles, B., Hruschka, E.R. Jr., Mitchell, T.M.: Toward an Architecture for Never-Ending Language Learning. In Proceedings of the Conference on Artificial Intelligence (AAAI) (2010)
17.
Mitchell, T., Cohen, W., Hruschka, E., et al.: Never-ending learning. In Proceedings of the Conference on Artificial Intelligence (AAAI) (2015)
18.
Fergus, R., Fei-Fei, L., Perona, P., et al.: Learning object categories from Google’s image search. Tenth IEEE International Conference on Computer Vision. IEEE, pp. 1816-1823 (2005)
19.
Nishizaki, H., Sekiguchi, Y.: Word Error Correction of Continuous Speech Recognition Using WEB Documents for Spoken Document Indexing. In: Matsumoto, Y., Sproat, R.W., Wong, K.F., Zhang, M. (eds.) Computer Processing of Oriental Languages. Beyond the Orient: The Research Challenges Ahead, ICCPOL (2006)
20.
Oertel, C., O’Shea, S., Bodnar, A., Blostein, D.: Using the web to validate document recognition results: experiments with business cards. Proc. SPIE, Document Recognition and Retrieval XII (2005)
21.
Donoser, M., Bischof, H., Wagner, S.: Using web search engines to improve text recognition. International Conference on Pattern Recognition, pp. 1–4 (2008)
22.
Donoser, M., Wagner, S., Bischof, H.: Context information from search engines for document recognition. Pattern Recogn. Lett. 31, 750–754 (2010)CrossRef
23.
Bellegarda, J.R.: Statistical language model adaptation: review and perspectives. Speech Commun. 42(1), 93–108 (2004)CrossRef
24.
Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. arXiv:​1810.​04805 (2019)
25.
Russell, B.C., Torralba, A., Murphy, K.P., et al.: LabelMe: a database and web-based tool for image annotation. Int. J. Comput. Vision 77(1–3), 157–173 (2008)CrossRef
26.
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: ImageNet: a large-scale hierarchical image database. IEEE Computer Vision and Pattern Recognition (CVPR) (2009)
27.
Li, F.-F. ImageNet: crowdsourcing, benchmarking & other cool things, CMU VASC Seminar, March (2010)
28.
Zhuo, H.H., Yang, Q., Pan, R., Li, L.: Cross-domain action-model acquisition for planning via web search. Twenty-First International Conference on Automated Planning and Scheduling (2011)
29.
Chen, L., Lamel, L., Gauvain, J.L., et al.: Dynamic language modeling for broadcast news. International Conference on Spoken Language Processing (2004)
30.
Whitelaw, C., Hutchinson, B., Chung, G.Y., et al.: Using the web for language independent spellchecking and autocorrection. Conference on Empirical Methods in Natural Language Processing: Volume. Association for Computational Linguistics, pp. 890-899 (2009)
31.
Bassil, Y., Alwani, M.: OCR post-processing error correction algorithm using Google’s online spelling suggestion. Emerg. Trends Comput. Inf. Sci. 3(1), 90–99 (2012)
32.
Oprean, C., Likforman-Sulem, L., Popescu, A., et al.: Using the Web to Create Dynamic Dictionaries in Handwritten Out-of-Vocabulary Word Recognition. International Conference on Document Analysis and Recognition, pp. 989-993 (2013)
33.
Oprean, C., Popescu, A., Popescu, A., et al.: Handwritten word recognition using Web resources and recurrent neural networks. IJDAR 18(4), 287–301 (2015)CrossRef
34.
Oprean, C., Likformansulem, L., Mokbel, C., et al.: BLSTM-based handwritten text recognition using Web resources. International Conference on Document Analysis and Recognition, pp. 466-470 (2015)
35.
Rosenfeld, R.: Two decades of statistical language modeling: Where do we go from here? Proc. IEEE 88(8), 1270–8 (2000)CrossRef
36.
Marti, U.V., Bunke, H.: Using a statistical language model to improve the performance of an HMM-based cursive handwriting recognition systems. Int. J. Pattern Recognit. Artif. Intell. 15(01), 65–90 (2001)CrossRef
37.
Li, N.-X., Jin, L.-W.: A Bayesian-based probabilistic model for unconstrained handwritten offline Chinese text line recognition. Proc. IEEE Int’l Conf. Systems, Man, and Cybernetics, pp. 3664 - 3668 (2010)
38.
Zhou, X.-D., Wang, D.-H., Tian, F., Liu, C.-L., Nakagawa, M.: Handwritten Chinese/Japanese text recognition using semi-markov conditional random fields. IEEE Trans. Pattern Anal. Mach. Intell. 35(10), 2413–2426 (2013)CrossRef
39.
Bengio, Y., Ducharme, R., Vincent, P., Jauvin, C.: A neural probabilistic language model. J. Mach. Learn. Res. 3, 1137–1155 (2003)MATH
40.
Mikolov, T., Karafiat, M., Burget, L., Cernocky, J. H., Khudanpur, S.: Recurrent neural network based language model. Proc. Interspeech, pp. 1045-1048 (2010)
41.
42.
Irie, K., Tüske, Z., Alkhouli, T., et al.: LSTM, GRU, highway and a bit of attention: an empirical overview for language modeling in speech recognition. INTERSPEECH, 519-3523 (2016)
43.
Luong, T., Kayser, M., Manning, C.D.: Deep neural language models for machine translation. Nineteenth Conference on Computational Natural Language Learning, 305-309 (2015)
44.
Bellegarda, J.R.: Exploiting latent semantic information in statistical language modeling. Proc. IEEE 88(8), 1279–1296 (2000)CrossRef
45.
Hofmann, T.: Unsupervised learning by probabilistic latent semantic analysis. Mach. Learn. 42(1–2), 177–196 (2001)CrossRefMATH
46.
Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. JMLR 3, 993–1022 (2003)MATH
47.
48.
Gao, J., Suzuki, H., Yuan, W.: An empirical study on language model adaptation. ACM Trans. Asian Lang. Inf. Process. 5(3), 209–227 (2006)CrossRef
49.
Liu, X., Gales, M.J.F., Woodland, P.C.: Use of contexts in language model interpolation and adaptation. Comput. Speech Lang. 27(1), 301–321 (2013)CrossRef
50.
Su, T.-H., Zhang, T.-W., Guan, D.-J.: Corpus-based HIT-MW database for offline recognition of general-purpose Chinese handwritten text. Int’l J. Doc. Anal. Recognit. 10(1), 27–38 (2007)CrossRef
51.
Liu, C.-L., Yin, F., Wang, D.-H., Wang, Q.-F.: CASIA Online and offline Chinese handwriting databases. Proc. 11th Int’l Conf. Document Analysis and Recognition, pp. 37-41, (2011)
52.
Yin, F., Wang, Q.-F., Zhang, X.-Y., Liu, C.-L.: ICDAR 2013 Chinese handwriting recognition competition. Proc. ICDAR, pp. 1464-1470 (2013)
53.
Su, T.-H., Zhang, T., Guan, D.-J., Huang, H.-J.: Off-line recognition of realistic Chinese handwriting using segmentation-free strategy. Pattern Recognit. 42(1), 167–182 (2009)
54.
Wang, Z.-R., Du, Jun, Hu, J.-S., Hu, Yu-Long: Deep convolutional neural network based hidden markov model for offline handwritten Chinese text recognition. Proc. ACPR (2017)
55.
Peng, D., Jin, L., Ma, W., Xie, C., Zhang, H., Zhu, S., Li, J.: Recognition of handwritten Chinese text by segmentation: A segment-annotation-free approach. IEEE Trans. Multimed. (2022)
56.
Wang, Z.-R., Du, J., Wang, J.-M.: Writer-aware CNN for parsimonious HMM-based offline handwritten Chinese text recognition. Pattern Recogn. 100, 107102 (2020)CrossRef
57.
Xu, L., Yin, F., Wang, Q.-F., Liu, C.-L.: An over-segmentation method for single touching Chinese handwriting with learning-based filtering. Int. J. Doc. Anal. Recognit. 17(1), 91–104 (2014)CrossRef
58.
Wang, Z.X., Wang, Q.F., Yin, F., Liu, C.L.: Weakly supervised learning for over-segmentation based handwritten Chinese text recognition. 17th International Conference on Frontiers in Handwriting Recognition (ICFHR) (2020)
59.
Peng, D.-Z., Jin, L.-W., Wu, Y.-Q., Wang, Z.-P., Cai, M.-X.: A Fast and Accurate Fully Convolutional Network for End-to-End Handwritten Chinese Text Segmentation and Recognition. In Proc. 15th International Conference on Document Analysis and Recognition, pp. 25-30 (2019)
60.
Fink, G.A.: Markov models for offline handwriting recognition: a survey. Springer-Verlag (2009)
61.
Messina, R., Louradour, J.: Segmentation-free handwritten Chinese text recognition with LSTM-RNN. Proc. Int’l Conf. on Document Analysis and Recognition (ICDAR), pp.171-175 (2015)
62.
Stolcke, A.: SRILM—An extensible language modeling toolkit. In: Proceedings of the 7th international conference on spoken language processing (ICSLP 2002) 901-904 (2002)
63.
Wang, S., Chen, L., Xu, L., Fan, W., Sun, J., Naoi, S.: Deep knowledge training and heterogeneous cnn for handwritten chinese text recognition. Proc. 15th International Conference on Frontiers of Handwriting Recognition, pp. 84-89 (2016)
64.
Xie, Z.-C., Huang, Y.-X., Zhu, Y.-Z., Jin, L.-W., Liu, Y.-L., Xie, L.-L.: Aggregation cross-entropy for sequence recognition. In Proc. the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6538-6547 (2019)
Metadaten
Titel
Retrieval-based language model adaptation for handwritten Chinese text recognition
verfasst von
Shuying Hu
Qiufeng Wang
Kaizhu Huang
Min Wen
Frans Coenen
Publikationsdatum
27.10.2022
Verlag
Springer Berlin Heidelberg
Erschienen in
International Journal on Document Analysis and Recognition (IJDAR) / Ausgabe 2/2023
Print ISSN: 1433-2833
Elektronische ISSN: 1433-2825
DOI
https://doi.org/10.1007/s10032-022-00419-2

Weitere Artikel der Ausgabe 2/2023

International Journal on Document Analysis and Recognition (IJDAR) 2/2023 Zur Ausgabe

Original Paper

Refocus attention span networks for handwriting line recognition

Original Paper

Tables to LaTeX: structure and content extraction from scientific tables

Original Paper

WriterINet: a multi-path deep CNN for offline text-independent writer identification

Original Paper

Adaptive dewarping of severely warped camera-captured document images based on document map generation

Premium Partner

    Bildnachweise