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Título:Wavelet descriptors for handwritten text recognition in historical documents
Autor:Seijas, L.M.; Bezerra, B.L.D.
Filiación:Departamento de Computación, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Buenos Aires, Argentina
Escola Politécnica de Pernambuco, Universidade de Pernambuco (UPE), Recife, Brazil
Palabras clave:Hand-written text recognition; Historical documents; Wavelet descriptors; Character recognition
Año:2017
Página de inicio:95
Página de fin:112
Título revista:Handwriting: Recognition, Development and Analysis
Título revista abreviado:Handwriting: Recognit., Development and Analysis
Registro:https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_97815361_v_n_p95_Seijas

Referencias:

  • Bazzi, I., Schwartz, R., Makhoul, J., An omnifont open-vocabulary OCR system for english and arabic (1999) IEEE Transactions on Pattern Analysis and Machine Intelligence, 21 (6), pp. 495-504
  • Bezerra, B.L.D., Zanchettin, C., de Andrade, V.B., (2012) A MDRNN-SVM Hybrid Model for Cursive Offline Handwriting Recognition, pp. 246-254. , Springer Berlin Heidelberg, Berlin, Heidelberg
  • Bluche, T., (2015) Deep Neural Networks for Large Vocabulary Handwritten Text Recognition., , Theses, Université Paris Sud-Paris XI
  • Bosch, V., Toselli, A.H., Vidal, E., Statistical text line analysis in handwritten documents (2012) 2012 International Conference on Frontiers in Handwriting Recognition., , Institute of Electrical and Electronics Engineers (IEEE)
  • Causer, T., Wallace, V., (2012) Building a volunteer community: Results and findings from Transcribe Bentham., , Digital Humanities Quarterly
  • Chen, C.-M., Chen, C.-C., Chen, C.-C., (2006) A comparison of texture features based on SVM and SOM.
  • Debnath, L., (2002) Wavelet Transforms and Their Applications., , Springer Nature
  • Dewangan, N., Goswami, A., (2012) Image Denoising Using Wavelet Thresholding Methods., 2, pp. 271-275
  • El-Hajj, R., Likforman-Sulem, L., Mokbel, C., Arabic handwriting recognition using baseline dependant features and hidden markov modeling (2005) Proceedings of the Eighth International Conference on Document Analysis and Recognition, pp. 893-897. , ICDAR '05, Washington, DC, USA. IEEE Computer Society
  • Espana-Boquera, S., Castro-Bleda, M.J., Gorbe-Moya, J., Zamora-Martinez, F., Improving offline handwritten text recognition with hybrid hmm/ann models (2011) IEEE Trans. Pattern Anal. Mach. Intell., 33 (4), pp. 767-779
  • Gouveia, F.M., Bezerra, B.L.D., Zanchettin, C., Meneses, J.R.J., Handwriting recognition system for mobile accessibility to the visually impaired people (2014) Systems, Man and Cybernetics SMC, (4), pp. 3918-3981. , 3
  • Günter, S., Bunke, H., HMM-based handwritten word recognition: On the optimization of the number of states, training iterations and gaussian components (2004) Pattern Recognition, 37 (10), pp. 2069-2079
  • Jelinek, F., (1998) Statistical Methods for Speech Recognition., , MIT Press
  • Kozielski, M., Forster, J., Ney, H., Moment-based image normalization for handwritten text recognition (2012) Proceedings of the 2012 International Conference on Frontiers in Handwriting Recognition, pp. 256-261. , ICFHR '12, Washington, DC, USA. IEEE Computer Society
  • Likforman-Sulem, L., Zahour, A., Taconet, B., (2006) Text line segmentation of historical documents: A survey., 9, pp. 123-138. , Springer Nature
  • Mallat, S., (1999) A Wavelet Tour of Signal Processing., , Academic Press
  • Marti, U.-V., Bunke, H., (2002) Hidden markov models. chapter Using a Statistical Language Model to Improve the Performance of a HMM-based Cursive Handwriting Recognition Systems, pp. 65-90. , World Scientific Publishing Co., Inc., River Edge, NJ, USA
  • Menasri, F., Likforman-Sulem, L., Mohamad, R.A.-H., Kermorvant, C., Bianne-Bernard, A.-L., Mokbel, C., Dynamic and contextual information in hmm modeling for handwritten word recognition (2011) IEEE Transactions on Pattern Analysis and Machine Intelligence, 33, pp. 2066-2080
  • Michal, K., Doetsch, P., Ney, H., Improvements in rwth’s system for off-line handwriting recognition (2013) 2013 12th International Conference on Document Analysis and Recognition., , Institute of Electrical and Electronics Engineers (IEEE)
  • Mohamad, R.A.-H., Likforman-Sulem, L., Mokbel, C., Combining slantedframe classifiers for improved hmm-based arabic handwriting recognition (2009) IEEE Transactions on Pattern Analysis and Machine Intelligence, 31 (7), pp. 1165-1177
  • Pastor, M., Sánchez, J., Toselli, A.H., Vidal, E., (2014) Handwritten Text Recognition:Word-Graphs, Keyword Spotting and Computer Assisted Transcription.
  • Pastor, M., Toselli, A., Vidal, E., (2004) Projection profile based algorithm for slant removal., pp. 183-190
  • Pastor, M., Toselli, A., Vidal, E., (2004) Projection profile based algorithm for slant removal., pp. 183-190
  • Pastor, M., Toselli, A.H., Vidal, E., (2006) Criteria for handwritten off-line text size normalization.
  • Patel, D.K., Som, T., Yadav, S.K., Singh, M.K., Handwritten character recognition using multiresolution technique and euclidean distance metric (2012) Journal of Signal and Information Processing, 3 (2), pp. 208-214
  • Romero, V., Toselli, A.H., Vidal, E., Multimodal Interactive Handwritten Text Transcription (2012) Series in Machine Perception and Artificial Intelligence (MPAI), , World Scientific
  • Sánchez, J.A., Bosch, V., Romero, V., Depuydt, K., de Does, J., Handwritten text recognition for historical documents in the transcriptorium project (2014) Proceedings of the First International Conference on Digital Access to Textual Cultural Heritage, pp. 111-117. , DATeCH '14, New York, NY, USA. ACM
  • Sánchez, J.A., Mühlberger, G., Gatos, B., Schofield, P., Depuydt, K., Davis, R.M., Vidal, E., de Does, J., Transcriptorium (2013) Proceedings of the 2013 ACM symposium on Document engineering-DocEng’13., , Association for Computing Machinery (ACM)
  • Sanchez, J.A., Toselli, A.H., Romero, V., Vidal, E., ICDAR 2015 competition HTRtS: Handwritten text recognition on the tranScriptorium dataset (2015) 2015 13th International Conference on Document Analysis and Recognition (ICDAR)., , Institute of Electrical and Electronics Engineers (IEEE)
  • Seijas, L.M., Segura, E.C., A wavelet-based descriptor for handwritten numeral classification (2012) 2012 International Conference on Frontiers in Handwriting Recognition., , Institute of Electrical and Electronics Engineers (IEEE)
  • Shao, Y., Chang, C.-H., Wavelet transform to hybrid support vector machine and hidden markov model for speech recognition (2005) 2005 IEEE International Symposium on Circuits and Systems., , Institute of Electrical and Electronics Engineers (IEEE)
  • Skodras, A., Christopoulos, C., Ebrahimi, T., JPEG2000: The upcoming still image compression standard (2001) Pattern Recognition Letters, 22 (12), pp. 1337-1345
  • Stolcke, A., (2002) SRILM-an extensible language modeling toolkit., , Proc. of ICSLP, Denver, USA
  • Toselli, A.H., Juan, A., González, J., Salvador, I., Vidal, E., Casacuberta, F., Keyers, D., Ney, H., INTEGRATED HANDWRITING RECOGNITION AND INTERPRETATION USING FINITE-STATE MODELS (2004) International Journal of Pattern Recognition and Artificial Intelligence, 18 (4), pp. 519-539
  • Toselli, A.H., Vidal, E., Handwritten text recognition results on the bentham collection with improved classical n-gram-hmm methods (2015) Proceedings of the 3rd InternationalWorkshop on Historical Document Imaging and Processing, pp. 15-22. , HIP '15, New York, NY, USA. ACM
  • Trivedi, N., Kumar, V., Singh, S., Ahuja, S., Chadha, R., Speech recognition by wavelet analysis (2011) International Journal of Computer Applications, 15 (8), pp. 27-32
  • Young, S., Evermann, G., Gales, M., Hain, T., Kershaw, D., (2009) The HTK Book:Hidden Markov Models Toolkit V3.4., , Microsoft Corporation Cambridge Research Laboratory Ltd

Citas:

---------- APA ----------
Seijas, L.M. & Bezerra, B.L.D. (2017) . Wavelet descriptors for handwritten text recognition in historical documents. Handwriting: Recognition, Development and Analysis, 95-112.
Recuperado de https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_97815361_v_n_p95_Seijas [ ]
---------- CHICAGO ----------
Seijas, L.M., Bezerra, B.L.D. "Wavelet descriptors for handwritten text recognition in historical documents" . Handwriting: Recognition, Development and Analysis (2017) : 95-112.
Recuperado de https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_97815361_v_n_p95_Seijas [ ]
---------- MLA ----------
Seijas, L.M., Bezerra, B.L.D. "Wavelet descriptors for handwritten text recognition in historical documents" . Handwriting: Recognition, Development and Analysis, 2017, pp. 95-112.
Recuperado de https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_97815361_v_n_p95_Seijas [ ]
---------- VANCOUVER ----------
Seijas, L.M., Bezerra, B.L.D. Wavelet descriptors for handwritten text recognition in historical documents. Handwriting: Recognit., Development and Analysis. 2017:95-112.
Available from: https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_97815361_v_n_p95_Seijas [ ]