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

Non-invasive multi-modal human identification system combining ECG, GSR, and airflow biosignals

A huge amount of data can be collected through a wide variety of sensor technologies. Data mining techniques are often useful for the analysis of gathered data. This paper studies the use of three wearable sensors that monitor the electrocardiogram, airflow, and galvanic skin response of a subject with the purpose of designing an efficient multi-modal human identification system. The proposed system, based on the rotation forest ensemble algorithm, offers a high accuracy (99.6 % true acceptance rate and just 0.1 % false positive rate). For its evaluation, the proposed system was testing against the characteristics commonly demanded in a biometric system, including universality, uniqueness, permanence, and acceptance. Finally, a proof-of-concept implementation of the system is demonstrated on a smartphone and its performance is evaluated in terms of processing speed and power consumption. The identification of a sample is extremely efficient, taking around 200 ms and consuming just a few millijoules. It is thus feasible to use the proposed system on a regular smartphone for user identification.This work was supported by MINECO grant TIN2013- 46469-R (SPINY: Security and Privacy in the Internet of You) and CAM grant S2013/ICE-3095 (CIBERDINE: Cybersecurity, Data, and Risks)

Automatic handwriter identification using advanced machine learning

Handwriter identification a challenging problem especially for forensic investigation. This topic has received significant attention from the research community and several handwriter identification systems were developed for various applications including forensic science, document analysis and investigation of the historical documents. This work is part of an investigation to develop new tools and methods for Arabic palaeography, which is is the study of handwritten material, particularly ancient manuscripts with missing writers, dates, and/or places. In particular, the main aim of this research project is to investigate and develop new techniques and algorithms for the classification and analysis of ancient handwritten documents to support palaeographic studies. Three contributions were proposed in this research. The first is concerned with the development of a text line extraction algorithm on colour and greyscale historical manuscripts. The idea uses a modified bilateral filtering approach to adaptively smooth the images while still preserving the edges through a nonlinear combination of neighboring image values. The proposed algorithm aims to compute a median and a separating seam and has been validated to deal with both greyscale and colour historical documents using different datasets. The results obtained suggest that our proposed technique yields attractive results when compared against a few similar algorithms. The second contribution proposes to deploy a combination of Oriented Basic Image features and the concept of graphemes codebook in order to improve the recognition performances. The proposed algorithm is capable to effectively extract the most distinguishing handwriter’s patterns. The idea consists of judiciously combining a multiscale feature extraction with the concept of grapheme to allow for the extraction of several discriminating features such as handwriting curvature, direction, wrinkliness and various edge-based features. The technique was validated for identifying handwriters using both Arabic and English writings captured as scanned images using the IAM dataset for English handwriting and ICFHR 2012 dataset for Arabic handwriting. The results obtained clearly demonstrate the effectiveness of the proposed method when compared against some similar techniques. The third contribution is concerned with an offline handwriter identification approach based on the convolutional neural network technology. At the first stage, the Alex-Net architecture was employed to learn image features (handwritten scripts) and the features obtained from the fully connected layers of the model. Then, a Support vector machine classifier is deployed to classify the writing styles of the various handwriters. In this way, the test scripts can be classified by the CNN training model for further classification. The proposed approach was evaluated based on Arabic Historical datasets; Islamic Heritage Project (IHP) and Qatar National Library (QNL). The obtained results demonstrated that the proposed model achieved superior performances when compared to some similar method

Recognition Technology for Four Arithmetic Operations

Numeral recognition is an important research direction in field of pattern recognition, and it has broad application prospects. Aiming at four arithmetic operations of general printed formats, this article adopts a multiple hybrid recognition method and is applied to automatically calculating. This method mainly uses BP neural network and template matching method to distinguish the numerals and operators, in order to increase the operation speed and recognition accuracy. Sample images of four arithmetic operations are extracted from printed books, and they are used for testing the performance of proposed recognition method. The experiments show that the method provides correct recognition rate of 96% and correct calculation rate of 89%

A study of the effects of ageing on the characteristics of handwriting and signatures

The work presented in this thesis is focused on the understanding of factors that are unique to the elderly and their use of biometric systems. In particular, an investigation is carried out with a focus on the handwritten signature as the biometric modality of choice. This followed on from an in-depth analysis of various biometric modalities such as voice, fingerprint and face. This analysis aimed at investigating the inclusivity of and the policy guiding the use of biometrics by the elderly. Knowledge gained from extracted features of the handwritten signatures of the elderly shed more light on and exposed the uniqueness of some of these features in their ability to separate the elderly from the young. Consideration is also given to a comparative analysis of another handwriting task, that of copying text both in cursive and block capitals. It was discovered that there are features that are unique to each task. Insight into the human perceptual capability in inspecting signatures, in assessing complexity and in judging imitations was gained by analysing responses to practical scenarios that applied human perceptual judgement. Features extracted from a newly created database containing handwritten signatures donated by elderly subjects allowed the possibility of analysing the intra-class variations that exist within the elderly population

Graphonomics and your Brain on Art, Creativity and Innovation : Proceedings of the 19th International Graphonomics Conference (IGS 2019 – Your Brain on Art)

[Italiano]: “Grafonomia e cervello su arte, creatività e innovazione”. Un forum internazionale per discutere sui recenti progressi nell'interazione tra arti creative, neuroscienze, ingegneria, comunicazione, tecnologia, industria, istruzione, design, applicazioni forensi e mediche. I contributi hanno esaminato lo stato dell'arte, identificando sfide e opportunità, e hanno delineato le possibili linee di sviluppo di questo settore di ricerca. I temi affrontati includono: strategie integrate per la comprensione dei sistemi neurali, affettivi e cognitivi in ambienti realistici e complessi; individualità e differenziazione dal punto di vista neurale e comportamentale; neuroaesthetics (uso delle neuroscienze per spiegare e comprendere le esperienze estetiche a livello neurologico); creatività e innovazione; neuro-ingegneria e arte ispirata dal cervello, creatività e uso di dispositivi di mobile brain-body imaging (MoBI) indossabili; terapia basata su arte creativa; apprendimento informale; formazione; applicazioni forensi. / [English]: “Graphonomics and your brain on art, creativity and innovation”. A single track, international forum for discussion on recent advances at the intersection of the creative arts, neuroscience, engineering, media, technology, industry, education, design, forensics, and medicine. The contributions reviewed the state of the art, identified challenges and opportunities and created a roadmap for the field of graphonomics and your brain on art. The topics addressed include: integrative strategies for understanding neural, affective and cognitive systems in realistic, complex environments; neural and behavioral individuality and variation; neuroaesthetics (the use of neuroscience to explain and understand the aesthetic experiences at the neurological level); creativity and innovation; neuroengineering and brain-inspired art, creative concepts and wearable mobile brain-body imaging (MoBI) designs; creative art therapy; informal learning; education; forensics

Large vocabulary off-line handwritten word recognition

Considerable progress has been made in handwriting recognition technology over the last few years. Thus far, handwriting recognition systems have been limited to small-scale and very constrained applications where the number on different words that a system can recognize is the key point for its performance. The capability of dealing with large vocabularies, however, opens up many more applications. In order to translate the gains made by research into large and very-large vocabulary handwriting recognition, it is necessary to further improve the computational efficiency and the accuracy of the current recognition strategies and algorithms. In this thesis we focus on efficient and accurate large vocabulary handwriting recognition. The main challenge is to speedup the recognition process and to improve the recognition accuracy. However. these two aspects are in mutual conftict. It is relatively easy to improve recognition speed while trading away some accuracy. But it is much harder to improve the recognition speed while preserving the accuracy. First, several strategies have been investigated for improving the performance of a baseline recognition system in terms of recognition speed to deal with large and very-large vocabularies. Next, we improve the performance in terms of recognition accuracy while preserving all the original characteristics of the baseline recognition system: omniwriter, unconstrained handwriting, and dynamic lexicons. The main contributions of this thesis are novel search strategies and a novel verification approach that allow us to achieve a 120 speedup and 10% accuracy improvement over a state-of-art baselinè recognition system for a very-large vocabulary recognition task (80,000 words). The improvements in speed are obtained by the following techniques: lexical tree search, standard and constrained lexicon-driven level building algorithms, fast two-level decoding algorithm, and a distributed recognition scheme. The recognition accuracy is improved by post-processing the list of the candidate N-best-scoring word hypotheses generated by the baseline recognition system. The list also contains the segmentation of such word hypotheses into characters . A verification module based on a neural network classifier is used to generate a score for each segmented character and in the end, the scores from the baseline recognition system and the verification module are combined to optimize performance. A rejection mechanism is introduced over the combination of the baseline recognition system with the verification module to improve significantly the word recognition rate to about 95% while rejecting 30% of the word hypotheses