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International Journal on Document Analysis and Recognition (IJDAR)
Published in: International Journal on Document Analysis and Recognition (IJDAR) 4/2022

07-09-2022 | Special Issue Paper

Combination of explicit segmentation with Seq2Seq recognition for fine analysis of children handwriting

Authors: Omar Krichen, Simon Corbillé, Éric Anquetil, Nathalie Girard, Élisa Fromont, Pauline Nerdeux

Published in: International Journal on Document Analysis and Recognition (IJDAR) | Issue 4/2022

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Abstract

We consider the task of analyzing children handwriting in the context of a dictation task. The objective is to detect orthographic and phonological errors. To achieve this goal, we extend an existing handwriting analysis engine, based on an explicit segmentation of the handwritten input, originally developed for children copying exercises. We present a new approach, based on the combination of this analysis engine with a deep learning word recognition approach in order to improve both the recognition and segmentation performance. Explicit segmentation needs prior knowledge, and the deep network recognition predictions are a reliable approximation of the ground truth which can guide the analysis process. We propose to combine multiple prior knowledge strategies to further improve the analysis performance. Furthermore, we exploit the deep network approximate implicit segmentation to optimize the existing analysis process in terms of complexity.
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Appendix
Available only for authorised users
Footnotes
Literature
1.
Simonnet, D., Girard, N., Anquetil, E., Renault, M., Thomas, S.: Evaluation of children cursive handwritten words for e-education. Pattern Recognit. Lett. 121, 133–139 (2019)CrossRef
2.
Marti, U.-V., Bunke, H.: A full english sentence database for off-line handwriting recognition. In: Fifth International Conference on Document Analysis and Recognition, ICDAR 1999, 20–22 September, 1999, Bangalore, India, pp. 705–708. IEEE Computer Society (1999)
3.
Liwicki, M., Bunke, H.: Iam-ondb—an on-line English sentence database acquired from handwritten text on a whiteboard. In: Eighth International Conference on Document Analysis and Recognition (ICDAR 2005), 29 August–1 September 2005, Seoul, Korea, pp. 956–961. IEEE Computer Society (2005)
4.
Tappert, C.C., Suen, C.Y., Wakahara, T.: The state of the art in online handwriting recognition. IEEE Trans. Pattern Anal. Mach. Intell. 12(8), 787–808 (1990)CrossRef
5.
Plamondon, R., Srihari, S.N.: Online and off-line handwriting recognition: a comprehensive survey. IEEE Trans. Pattern Anal. Mach. Intell. 22(1), 63–84 (2000)CrossRef
6.
Graves, A., Fernández, S., Gomez, F. J., Schmidhuber, J.: Connectionist temporal classification: labelling unsegmented sequence data with recurrent neural networks. In: Machine Learning, Proceedings of the Twenty-Third International Conference (ICML 2006), Pittsburgh, Pennsylvania, USA, June 25–29, 2006, Volume 148 of ACM International Conference Proceeding Series, pp. 369–376. ACM (2006)
7.
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)CrossRef
8.
Graves, A., Liwicki, M., Fernández, S., Bertolami, R., Bunke, H., Schmidhuber, J.: A novel connectionist system for unconstrained handwriting recognition. IEEE Trans. Pattern Anal. Mach. Intell. 31(5), 855–868 (2009)CrossRef
9.
Carbune, V., Gonnet, P., Deselaers, T., Rowley, H.A., Daryin, A.N., Calvo, M., Wang, L.-L., Keysers, D., Feuz, S., Gervais, P.: Fast multi-language LSTM-based online handwriting recognition. Int. J. Doc. Anal. Recognit. 23(2), 89–102 (2020)CrossRef
10.
Shi, B., Bai, X., Yao, C.: An end-to-end trainable neural network for image-based sequence recognition and its application to scene text recognition. IEEE Trans. Pattern Anal. Mach. Intell. 39(11), 2298–2304 (2017)CrossRef
11.
Puigcerver, J.: Are multidimensional recurrent layers really necessary for handwritten text recognition? In: 14th IAPR International Conference on Document Analysis and Recognition, ICDAR 2017, Kyoto, Japan, November 9–15, 2017, pp. 67–72. IEEE (2017)
12.
Michael, J., Labahn, R., Grüning, T., Zöllner, J.: Evaluating sequence-to-sequence models for handwritten text recognition. In: 2019 International Conference on Document Analysis and Recognition, ICDAR 2019, Sydney, Australia, September 20–25, 2019, 1286–1293. IEEE (2019)
13.
Kang, L., Riba, P., Rusiñol, M., Fornés, A., Villegas, M.: Pay attention to what you read: non-recurrent handwritten text-line recognition. CoRR, abs/2005.13044 (2020)
14.
Barrere, K., Soullard Y., Lemaitre A., Coüasnon, B.: Transformers for historical handwritten text recognition. In: Doctoral Consortium: ICDAR 2021, Lausanne, Switzerland, September. Nibal Nayef and Jean-Christophe Burie (2021)
15.
Liu, H., Jin, S., Zhang, C.: Connectionist temporal classification with maximum entropy regularization. In: Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, NeurIPS 2018, December 3–8, 2018, Montréal, Canada, pp. 839–849 (2018)
16.
Gao, L., Zhang, H. Liu, C.-L.: Handwritten text recognition with convolutional prototype network and most aligned frame based CTC training. In: 16th International Conference on Document Analysis and Recognition, ICDAR 2021, Lausanne, Switzerland, September 5–10, 2021, Proceedings, Part I, Volume 12821 of Lecture Notes in Computer Science, pp. 205–220. Springer (2021)
17.
LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998)CrossRef
18.
Anquetil, E., Lorette, G.: On-line handwriting character recognition system based on hierarchical qualitative fuzzy modelling. In: Progress in Handwriting Recognition, pp. 109–116 (1997)
19.
Simonnet, D., Anquetil, E., Bouillon, M.: Multi-criteria handwriting quality analysis with online fuzzy models. Pattern Recognit. 69, 310–324 (2017)CrossRef
20.
Krichen, O., Corbillé, S., Anquetil, E., Girard, N., Nerdeux, P.: Online analysis of children handwritten words in dictation context. In: 14th International Workshop on Graphics Recognition, Lausanne, Switzerland, September (2021)
21.
Novak, J. R., Minematsu, N., Hirose, K., Hori, C., Kashioka, H., Dixon, P. R.: Improving wfst-based G2P conversion with alignment constraints and RNNLM n-best rescoring. In: INTERSPEECH 2012, 13th Annual Conference of the International Speech Communication Association, Portland, Oregon, USA, September 9–13, 2012, pp. 2526–2529. ISCA (2012)
22.
Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. In: 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7–9, 2015, Conference Track Proceedings
23.
Bluche, T., Messina, R.O.: Gated convolutional recurrent neural networks for multilingual handwriting recognition. In: 14th IAPR International Conference on Document Analysis and Recognition, ICDAR 2017, Kyoto, Japan, November 9–15, 2017, pp. 646–651. IEEE (2017)
24.
Almaksour, A., Anquetil, É.: Improving premise structure in evolving Takagi–Sugeno neuro-fuzzy classifiers. Evol. Syst. 2(1), 25–33 (2011)CrossRef
25.
Schwenk, H., Gauvain, J.-L.: Improved rover using language model information. 11 (2000)
26.
Damerau, F.: A technique for computer detection and correction of spelling errors. Commun. ACM 7(3), 171–176 (1964)CrossRef
Metadata
Title
Combination of explicit segmentation with Seq2Seq recognition for fine analysis of children handwriting
Authors
Omar Krichen
Simon Corbillé
Éric Anquetil
Nathalie Girard
Élisa Fromont
Pauline Nerdeux
Publication date
07-09-2022
Publisher
Springer Berlin Heidelberg
Published in
International Journal on Document Analysis and Recognition (IJDAR) / Issue 4/2022
Print ISSN: 1433-2833
Electronic ISSN: 1433-2825
DOI
https://doi.org/10.1007/s10032-022-00409-4

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