A survey on Off-Line Cursive Word Recognition

This paper presents a survey on Off-Line Cursive Script Recognition. The approaches to the problem are described into detail. Each step of the process leading from raw data to the final result is analyzed.\\ This survey is divided into two parts, the first one dealing with the general aspects of Cursive Script Recognition, the second one focusing on the applications presented in the literature

Turkish handwritten text recognition: a case of agglutinative languages

We describe a system for recognizing unconstrained Turkish handwritten text. Turkish has agglutinative morphology and theoretically an infinite number of words that can be generated by adding more suffixes to the word. This makes lexicon-based recognition approaches, where the most likely word is selected among all the alternatives in a lexicon, unsuitable for Turkish. We describe our approach to the problem using a Turkish prefix recognizer. First results of the system demonstrates the promise of this approach, with top-10 word recognition rate of about 40% for a small test data of mixed handprint and cursive writing. The lexicon-based approach with a 17,000 word-lexicon (with test words added) achieves 56% top-10 word recognition rate

Component-based Segmentation of words from handwritten Arabic text

Efficient preprocessing is very essential for automatic recognition of handwritten documents. In this paper, techniques on segmenting words in handwritten Arabic text are presented. Firstly, connected components (ccs) are extracted, and distances among different components are analyzed. The statistical distribution of this distance is then obtained to determine an optimal threshold for words segmentation. Meanwhile, an improved projection based method is also employed for baseline detection. The proposed method has been successfully tested on IFN/ENIT database consisting of 26459 Arabic words handwritten by 411 different writers, and the results were promising and very encouraging in more accurate detection of the baseline and segmentation of words for further recognition

ANN-based Innovative Segmentation Method for Handwritten text in Assamese

Artificial Neural Network (ANN) s has widely been used for recognition of optically scanned character, which partially emulates human thinking in the domain of the Artificial Intelligence. But prior to recognition, it is necessary to segment the character from the text to sentences, words etc. Segmentation of words into individual letters has been one of the major problems in handwriting recognition. Despite several successful works all over the work, development of such tools in specific languages is still an ongoing process especially in the Indian context. This work explores the application of ANN as an aid to segmentation of handwritten characters in Assamese- an important language in the North Eastern part of India. The work explores the performance difference obtained in applying an ANN-based dynamic segmentation algorithm compared to projection- based static segmentation. The algorithm involves, first training of an ANN with individual handwritten characters recorded from different individuals. Handwritten sentences are separated out from text using a static segmentation method. From the segmented line, individual characters are separated out by first over segmenting the entire line. Each of the segments thus obtained, next, is fed to the trained ANN. The point of segmentation at which the ANN recognizes a segment or a combination of several segments to be similar to a handwritten character, a segmentation boundary for the character is assumed to exist and segmentation performed. The segmented character is next compared to the best available match and the segmentation boundary confirmed

Text Line Segmentation of Historical Documents: a Survey

There is a huge amount of historical documents in libraries and in various National Archives that have not been exploited electronically. Although automatic reading of complete pages remains, in most cases, a long-term objective, tasks such as word spotting, text/image alignment, authentication and extraction of specific fields are in use today. For all these tasks, a major step is document segmentation into text lines. Because of the low quality and the complexity of these documents (background noise, artifacts due to aging, interfering lines),automatic text line segmentation remains an open research field. The objective of this paper is to present a survey of existing methods, developed during the last decade, and dedicated to documents of historical interest.Comment: 25 pages, submitted version, To appear in International Journal on Document Analysis and Recognition, On line version available at http://www.springerlink.com/content/k2813176280456k3

Handwriting recognition by using deep learning to extract meaningful features

[EN] Recent improvements in deep learning techniques show that deep models can extract more meaningful data directly from raw signals than conventional parametrization techniques, making it possible to avoid specific feature extraction in the area of pattern recognition, especially for Computer Vision or Speech tasks. In this work, we directly use raw text line images by feeding them to Convolutional Neural Networks and deep Multilayer Perceptrons for feature extraction in a Handwriting Recognition system. The proposed recognition system, based on Hidden Markov Models that are hybridized with Neural Networks, has been tested with the IAM Database, achieving a considerable improvement.Work partially supported by the Spanish MINECO and FEDER founds under project TIN2017-85854-C4-2-R.Pastor Pellicer, J.; Castro-Bleda, MJ.; España Boquera, S.; Zamora-Martinez, FJ. (2019). Handwriting recognition by using deep learning to extract meaningful features. AI Communications. 32(2):101-112. https://doi.org/10.3233/AIC-170562S101112322Baldi, P., Brunak, S., Frasconi, P., Soda, G., & Pollastri, G. (1999). Exploiting the past and the future in protein secondary structure prediction. Bioinformatics, 15(11), 937-946. doi:10.1093/bioinformatics/15.11.937LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444. doi:10.1038/nature14539Bertolami, R., & Bunke, H. (2008). Hidden Markov model-based ensemble methods for offline handwritten text line recognition. Pattern Recognition, 41(11), 3452-3460. doi:10.1016/j.patcog.2008.04.003Bianne-Bernard, A.-L., Menasri, F., Mohamad, R. A.-H., Mokbel, C., Kermorvant, C., & Likforman-Sulem, L. (2011). Dynamic and Contextual Information in HMM Modeling for Handwritten Word Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 33(10), 2066-2080. doi:10.1109/tpami.2011.22C.M. Bishop, Neural networks for pattern recognition, Oxford University Press, 1995.T. Bluche, H. Ney and C. Kermorvant, Feature extraction with convolutional neural networks for handwritten word recognition, in: 12th International Conference on Document Analysis and Recognition (ICDAR), 2013, pp. 285–289.T. Bluche, H. Ney and C. Kermorvant, Tandem HMM with convolutional neural network for handwritten word recognition, in: 38th International Conference on Acoustics Speech and Signal Processing (ICASSP), 2013, pp. 2390–2394.T. Bluche, H. Ney and C. Kermorvant, A comparison of sequence-trained deep neural networks and recurrent neural networks optical modeling for handwriting recognition, in: Slsp-2014, 2014, pp. 1–12.H. Bourlard and N. Morgan, Connectionist Speech Recognition – A Hybrid Approach, Series in Engineering and Computer Science, Vol. 247, Kluwer Academic, 1994.Bozinovic, R. M., & Srihari, S. N. (1989). Off-line cursive script word recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 11(1), 68-83. doi:10.1109/34.23114H. Bunke, Recognition of cursive roman handwriting – past, present and future, in: International Conference on Document Analysis and Recognition, Vol. 1, 2003, pp. 448–459.E. Caillault, C. Viard-Gaudin and A. Rahim Ahmad, MS-TDNN with global discriminant trainings, in: International Conference on Document Analysis and Recognition (ICDAR), 2005, pp. 856–860.P. Doetsch, M. Kozielski and H. Ney, Fast and robust training of recurrent neural networks for offline handwriting recognition, in: 14th International Conference on Frontiers in Handwriting Recognition (ICFHR), 2014, pp. 279–284.P. Dreuw, P. Doetsch, C. Plahl and H. Ney, Hierarchical hybrid MLP/HMM or rather MLP features for a discriminatively trained Gaussian HMM: A comparison for offline handwriting recognition, in: International Conference on Image Processing (ICIP), 2011, pp. 3541–3544.Dreuw, P., Heigold, G., & Ney, H. (2011). Confidence- and margin-based MMI/MPE discriminative training for off-line handwriting recognition. International Journal on Document Analysis and Recognition (IJDAR), 14(3), 273-288. doi:10.1007/s10032-011-0160-xEspaña-Boquera, S., Castro-Bleda, M. J., Gorbe-Moya, J., & Zamora-Martinez, F. (2011). Improving Offline Handwritten Text Recognition with Hybrid HMM/ANN Models. IEEE Transactions on Pattern Analysis and Machine Intelligence, 33(4), 767-779. doi:10.1109/tpami.2010.141A. Graves, S. Fernández, F. Gomez and J. Schmidhuber, Connectionist temporal classification: Labelling unsegmented sequence data with recurrent neural networks, in: 23rd International Conference on Machine Learning (ICML), ACM, 2006, pp. 369–376.A. Graves and N. Jaitly, Towards end-to-end speech recognition with recurrent neural networks, in: 31st International Conference on Machine Learning (ICML), 2014, pp. 1764–1772.Graves, A., Liwicki, M., Fernandez, S., Bertolami, R., Bunke, H., & Schmidhuber, J. (2009). A Novel Connectionist System for Unconstrained Handwriting Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 31(5), 855-868. doi:10.1109/tpami.2008.137A. Graves and J. Schmidhuber, Framewise phoneme classification with bidirectional LSTM networks, in: International Joint Conference on Neural Networks (IJCNN), Vol. 4, 2005, pp. 2047–2052.A. Graves and J. Schmidhuber, Offline handwriting recognition with multidimensional recurrent neural networks, in: Advances in Neural Information Processing Systems (NIPS), 2009, pp. 545–552.F. Grézl, M. Karafiát, S. Kontár and J. Černocký, Probabilistic and bottle-neck features for LVCSR of meetings, in: International Conference on Acoustics, Speech and Signal Processing (ICASSP), Vol. 4, 2007.Hochreiter, S., & Schmidhuber, J. (1997). Long Short-Term Memory. Neural Computation, 9(8), 1735-1780. doi:10.1162/neco.1997.9.8.1735Impedovo, S. (2014). More than twenty years of advancements on Frontiers in handwriting recognition. Pattern Recognition, 47(3), 916-928. doi:10.1016/j.patcog.2013.05.027Jaeger, S., Manke, S., Reichert, J., & Waibel, A. (2001). Online handwriting recognition: the NPen++ recognizer. International Journal on Document Analysis and Recognition, 3(3), 169-180. doi:10.1007/pl00013559M. Kozielski, P. Doetsch and H. Ney, Improvements in RWTH’s system for off-line handwriting recognition, in: 12th International Conference on Document Analysis and Recognition (ICDAR), IEEE, 2013, pp. 935–939.A. Krizhevsky, I. Sutskever and G.E. Hinton, ImageNet classification with deep convolutional neural networks, in: Advances in Neural Information Processing Systems (NIPS), F. Pereira, C.J.C. Burges, L. Bottou and K.Q. Weinberger, eds, Vol. 25, Curran Associates, Inc., 2012, pp. 1097–1105.Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11), 2278-2324. doi:10.1109/5.726791M. Liwicki, A. Graves, H. Bunke and J. Schmidhuber, A novel approach to on-line handwriting recognition based on bidirectional long short-term memory networks, in: 9th International Conference on Document Analysis and Recognition (ICDAR), 2007, pp. 367–371.Marti, U.-V., & Bunke, H. (2002). The IAM-database: an English sentence database for offline handwriting recognition. International Journal on Document Analysis and Recognition, 5(1), 39-46. doi:10.1007/s100320200071S. Marukatat, T. Artieres, R. Gallinari and B. Dorizzi, Sentence recognition through hybrid neuro-Markovian modeling, in: 6th International Conference on Document Analysis and Recognition (ICDAR), 2001, pp. 731–735.F.J. Och, Minimum error rate training in statistical machine translation, in: 41st Annual Meeting on Association for Computational Linguistics, ACL’03, Vol. 1, 2003, pp. 160–167.J. Pastor-Pellicer, S. España-Boquera, M.J. Castro-Bleda and F. Zamora-Martínez, A combined convolutional neural network and dynamic programming approach for text line normalization, in: 13th International Conference on Document Analysis and Recognition (ICDAR), 2015.J. Pastor-Pellicer, S. España-Boquera, F. Zamora-Martínez, M. Zeshan Afzal and M.J. Castro-Bleda, Insights on the use of convolutional neural networks for document image binarization, in: The International Work-Conference on Artificial Neural Networks, Vol. 9095, 2015, pp. 115–126.V. Pham, T. Bluche, C. Kermorvant and J. Louradour, Dropout improves recurrent neural networks for handwriting recognition, in: International Conference on Frontiers in Handwriting Recognition (ICFHR), 2014, pp. 285–290.Plamondon, R., & Srihari, S. N. (2000). Online and off-line handwriting recognition: a comprehensive survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(1), 63-84. doi:10.1109/34.824821Plötz, T., & Fink, G. A. (2009). Markov models for offline handwriting recognition: a survey. International Journal on Document Analysis and Recognition (IJDAR), 12(4), 269-298. doi:10.1007/s10032-009-0098-4A. Poznanski and L. Wolf, CNN-N-gram for HandwritingWord recognition, in: Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 2305–2314.Puigcerver, J. (2017). Are Multidimensional Recurrent Layers Really Necessary for Handwritten Text Recognition? 2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR). doi:10.1109/icdar.2017.20L.R. Rabiner, A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition, 1989.Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., … Fei-Fei, L. (2015). ImageNet Large Scale Visual Recognition Challenge. International Journal of Computer Vision, 115(3), 211-252. doi:10.1007/s11263-015-0816-yT.N. Sainath, B. Kingsbury and B. Ramabhadran, Auto-encoder bottleneck features using deep belief networks, in: International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2012, pp. 4153–4156.Sayre, K. M. (1973). Machine recognition of handwritten words: A project report. Pattern Recognition, 5(3), 213-228. doi:10.1016/0031-3203(73)90044-7Schenkel, M., Guyon, I., & Henderson, D. (1995). On-line cursive script recognition using time-delay neural networks and hidden Markov models. Machine Vision and Applications, 8(4), 215-223. doi:10.1007/bf01219589Schuster, M., & Paliwal, K. K. (1997). Bidirectional recurrent neural networks. IEEE Transactions on Signal Processing, 45(11), 2673-2681. doi:10.1109/78.650093A.W. Senior and A.J. Robinson, An off-line cursive handwriting recognition system, in: IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 20, 1998, pp. 309–321.E. Singer and R.P. Lippman, A speech recognizer using radial basis function neural networks in an HMM framework, in: International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Vol. 1, IEEE, 1992, pp. 629–632.J. Stadermann, A hybrid SVM/HMM acoustic modeling approach to automatic speech recognition, in: International Conference on Spoken Language Processing (ICSLP), 2004.A. Stolcke, SRILM: An extensible language modeling toolkit, in: International Conference on Spoken Language Processing (ICSLP), 2002, pp. 901–904.C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke and A. Rabinovich, Going deeper with convolutions, in: Conference on Computer Vision and Pattern Recognition (CVPR), 2015, pp. 1–12.TOSELLI, A. H., JUAN, A., GONZÁLEZ, J., SALVADOR, I., VIDAL, E., CASACUBERTA, F., … NEY, H. (2004). INTEGRATED HANDWRITING RECOGNITION AND INTERPRETATION USING FINITE-STATE MODELS. International Journal of Pattern Recognition and Artificial Intelligence, 18(04), 519-539. doi:10.1142/s0218001404003344Toselli, A. H., Romero, V., Pastor, M., & Vidal, E. (2010). Multimodal interactive transcription of text images. Pattern Recognition, 43(5), 1814-1825. doi:10.1016/j.patcog.2009.11.019J.M. Vilar, Efficient computation of confidence intervals for word error rates, in: International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2008, pp. 5101–5104.Vinciarelli, A. (2002). A survey on off-line Cursive Word Recognition. Pattern Recognition, 35(7), 1433-1446. doi:10.1016/s0031-3203(01)00129-7Voigtlaender, P., Doetsch, P., & Ney, H. (2016). Handwriting Recognition with Large Multidimensional Long Short-Term Memory Recurrent Neural Networks. 2016 15th International Conference on Frontiers in Handwriting Recognition (ICFHR). doi:10.1109/icfhr.2016.0052E. Wang, Q. Zhang, B. Shen, G. Zhang, X. Lu, Q. Wu and Y. Wang, Intel math kernel library, in: High-Performance Computing on the Intel® Xeon Phi™, Springer, 2014, pp. 167–188.F. Zamora-Martínez et al., April-ANN Toolkit, a Pattern Recognizer in Lua, Artificial Neural Networks Module, 2013, https://github.com/pakozm/ [github.com]april-ann.Zamora-Martínez, F., Frinken, V., España-Boquera, S., Castro-Bleda, M. J., Fischer, A., & Bunke, H. (2014). Neural network language models for off-line handwriting recognition. Pattern Recognition, 47(4), 1642-1652. doi:10.1016/j.patcog.2013.10.020Zeyer, A., Beck, E., Schlüter, R., & Ney, H. (2017). CTC in the Context of Generalized Full-Sum HMM Training. Interspeech 2017. doi:10.21437/interspeech.2017-107