- -

A Spanish dataset for reproducible benchmarked offline handwriting recognition

RiuNet: Repositorio Institucional de la Universidad Politécnica de Valencia

Compartir/Enviar a

Citas

Estadísticas

  • Estadisticas de Uso

A Spanish dataset for reproducible benchmarked offline handwriting recognition

Mostrar el registro sencillo del ítem

Ficheros en el ítem

Thumbnail
Nombre: EspanaCastro-Bleda ...
Tamaño: 507.8Kb
Formato: PDF
Descripción: Versión editorial
Abrir
dc.contributor.author España Boquera, Salvador es_ES
dc.contributor.author Castro-Bleda, Maria Jose es_ES
dc.date.accessioned 2023-10-19T18:02:15Z
dc.date.available 2023-10-19T18:02:15Z
dc.date.issued 2022-09 es_ES
dc.identifier.issn 1574-020X es_ES
dc.identifier.uri http://hdl.handle.net/10251/198426
dc.description.abstract [EN] In this paper, a public dataset for Offline Handwriting Recognition, along with an appropriate evaluation method to provide benchmark indicators at sentence level, is presented. This dataset, called SPA-Sentences, consists of offline handwritten Spanish sentences extracted from 1617 forms produced by the same number of writers. A total of 13,691 sentences comprising around 100,000 word instances out of a vocabulary of 3288 words occur in the collection. Careful attention has been paid to make the baseline experiments both reproducible and competitive. To this end, experiments are based on state-of-the-art recognition techniques combining convolutional blocks with one-dimensional Bidirectional Long Short Term Memory (LSTM) networks using Connectionist Temporal Classification (CTC) decoding. The scripts with the entire experimental setting have been made available. The SPA-Sentences dataset and its baseline evaluation are freely available for research purposes via the institutional University repository. We expect the research community to include this corpus, as is usually done with English IAM and French RIMES datasets, in their battery of experiments when reporting novel handwriting recognition techniques. es_ES
dc.language Inglés es_ES
dc.publisher Springer-Verlag es_ES
dc.relation.ispartof Language Resources and Evaluation es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject Handwriting recognition es_ES
dc.subject Offline handwriting recognition es_ES
dc.subject Datasets es_ES
dc.subject Evaluation es_ES
dc.subject Benchmarking es_ES
dc.subject Experimental reproducibility es_ES
dc.subject Spanish resources es_ES
dc.subject Deep learning es_ES
dc.subject Convolutional neural networks (CNN) es_ES
dc.subject Long short term memory (LSTM) networks es_ES
dc.subject Connectionist temporal classification (CTC) es_ES
dc.subject.classification LENGUAJES Y SISTEMAS INFORMATICOS es_ES
dc.title A Spanish dataset for reproducible benchmarked offline handwriting recognition es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1007/s10579-022-09587-3 es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Escola Tècnica Superior d'Enginyeria Informàtica es_ES
dc.description.bibliographicCitation España Boquera, S.; Castro-Bleda, MJ. (2022). A Spanish dataset for reproducible benchmarked offline handwriting recognition. Language Resources and Evaluation. 56(3):1009-1022. https://doi.org/10.1007/s10579-022-09587-3 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.1007/s10579-022-09587-3 es_ES
dc.description.upvformatpinicio 1009 es_ES
dc.description.upvformatpfin 1022 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 56 es_ES
dc.description.issue 3 es_ES
dc.relation.pasarela S\491455 es_ES
dc.description.references Amengual, J. C., Benedí, J. M., Casacuberta, F., Castaño, A., Castellanos, A., Jiménez, V. M., Llorens, D., Marzal, A., Prat, F., Vilar, J.M., Benedí, J.M., Casacuberta, F., Pastor, M., & Vidal. E. (2000). The EUTRANS-I speech translation system. Machine Translation Journal, 15, 75–103. es_ES
dc.description.references Amodei, D., Anubhai, R., Battenberg, E., Case, C., Casper, J., Catanzaro, B., Chen, J., Chrzanowski, M., Coates, A., Diamos, G., Elsen, E., Engel, J., Fan, L., Fougner, C., Han, T., Hannun, A., Jun, B., LeGresley, P., Lin, L., Narang, S., Ng, A., Ozair, S., Prenger, R., Raiman, J., Satheesh, S., Seetapun, D., Sengupta, S., Wang, Y., Wang, Z., Wang, C., Xiao, B., Yogatama, D., Zhan, J., & Zhu. Z. (2016). Deep speech 2: End-to-end speech recognition in English and Mandarin. In Proceedings of the 33rd international conference on international conference on machine learning (ICML) (Vol. 48, pp. 173–182). JMLR.org. es_ES
dc.description.references Chetlur, S., Woolley, C., Vandermersch, P., Cohen, J., Tran, J., Catanzaro, B., & Shelhamer, E. (2014). cuDNN: Efficient primitives for deep learning. CoRR abs/1410.0759. http://arxiv.org/abs/1410.0759. es_ES
dc.description.references Collobert, R., Kavukcuoglu, K., & Farabet, C. (2011). Torch7: A Matlab-like environment for machine learning. In Proceedings of big learning 2011: NIPS 2011 workshop on algorithms, systems, and tools for learning at scale. es_ES
dc.description.references Díaz-Verdejo, J. E., Peinado, A. M., Rubio, A. J., Segarra, E., Prieto, N., & Casacuberta, F. (1998). ALBAYZIN: A task-oriented Spanish speech corpus. In Proceedings of the first international conference on language resources and evaluation (LREC) (pp. 497–501). Granada, Spain. es_ES
dc.description.references Doetsch, P., Kozielski, M., & Ney, H. (2014). Fast and robust training of recurrent neural networks for offline handwriting recognition. In Proceedings of the 14th international conference on frontiers in handwriting recognition (ICFHR) (pp. 279–284). IEEE. es_ES
dc.description.references España Boquera, S., Castro Bleda, M. J., & Hidalgo, J. L. (2004). The SPARTACUS-Database: A Spanish sentence database for offline handwriting recognition. In Proceedings of the fourth international conference on language resources and evaluation (LREC) (pp. 227–230). Lisbon, Portugal. es_ES
dc.description.references Fischer, A., Baechler, M., Garz, A., Liwicki, M., & Ingold, R. (2014). A combined system for text line extraction and handwriting recognition in historical documents. In Proceedings of the 11th IAPR international workshop on document analysis systems (DAS) (pp. 71–75). IEEE. es_ES
dc.description.references Fischer, A., Indermühle, E., Bunke, H., Viehhauser, G., & Stolz, M. (2010). Ground Truth Creation for Handwriting Recognition in Historical Documents. In Proceedings of the 9th IAPR international workshop on document analysis systems (DAS) (pp. 3–10). ACM, New York, NY, USA. https://doi.org/10.1145/1815330.1815331. es_ES
dc.description.references Gers, F. A., Schraudolph, N. N., & Schmidhuber, J. (2002). Learning precise timing with LSTM recurrent networks. Journal of machine learning research, 3(Aug), 115–143. es_ES
dc.description.references Graves, A., Fernández, S., Gomez, F., & Schmidhuber, J. (2006). Connectionist temporal classification: labelling unsegmented sequence data with recurrent neural networks. In Proceedings of the 23rd international conference on machine learning (ICML) (pp. 369–376). ACM. es_ES
dc.description.references Graves, A., Liwicki, M., Fernández, S., Bertolami, R., Bunke, H., & Schmidhuber, J. (2008). A novel connectionist system for unconstrained handwriting recognition. IEEE Transaction on Pattern Analysis and Machine Intelligence, 31(5), 855–868. es_ES
dc.description.references Graves, A., & Schmidhuber, J. (2005). Framewise phoneme classification with bidirectional LSTM and other neural network architectures. Neural Networks, 18(5–6), 602–610. es_ES
dc.description.references Graves, A., & Schmidhuber, J. (2009). Offline handwriting recognition with multidimensional recurrent neural networks. In Advances in neural information processing systems, pp. 545–552. es_ES
dc.description.references Grosicki, E., Carré, M., Brodin, J. M., & Geoffrois, E. (2008). RIMES evaluation campaign for handwritten mail processing. In Proceedings of the 11th international conference on frontiers in handwriting recognition (ICFHR), pp. 1–6. Concordia University, Montreal, Canada. https://hal.archives-ouvertes.fr/hal-01395332. es_ES
dc.description.references Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735–1780. es_ES
dc.description.references Hull, J. J. (1994). A database for handwritten text recognition research. IEEE Transaction on Pattern Analysis and Machine Intelligence, 16(5), 550–554. es_ES
dc.description.references Hussain, R., Raza, A., Siddiqi, I., Khurshid, K., & Djeddi, C. (2015). A comprehensive survey of handwritten document benchmarks: structure, usage and evaluation (p. 46). Image and Video Processing: EURASIP J. es_ES
dc.description.references Juan, A., Toselli, A. H., Domnech, J., González, J., Salvador, I., Vidal, E., & Casacuberta, F. (2004). Integrated handwriting recognition and interpretation via finite-state models. International Journal of Pattern Recognition and Artificial Intelligence, 18(04), 519–539. es_ES
dc.description.references LeCun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998). Gradient-based learning applied to document recognition. In Proceedings of the IEEE, 86(11), 2278–2324 es_ES
dc.description.references Maas, A. L., Hannun, A. Y., & Ng, A. Y. (2013). Rectifier nonlinearities improve neural network acoustic models. In Proceedings of the international conference on international conference on machine learning (ICML) (Vol. 30, p. 3). es_ES
dc.description.references 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, 39–46. es_ES
dc.description.references Mocholí Calvo, C., Mocholí-Calvo Mocholí-Calvo, C. Tutored by E. VIdal and J. Puigcerver. (2017–2018). Development and experimentation of a deep learning system for convolutional and recurrent neural networks. Master’s thesis, ETSINF Universitat Politècnica de València, Valencia (Spain). es_ES
dc.description.references Paszke, A., Gross, S., Chintala, S., Chanan, G., Yang, E., DeVito, Z., Lin, Z., Desmaison, A., Antiga, L., & Lerer, A. (2017). Automatic differentiation in PyTorch. In Proceedings of the 31st conference on neural information processing systems (NIPS). Long Beach, CA, USA. es_ES
dc.description.references Pérez, D., Tarazón, L., Serrano, N., Castro, F., Terrades, O.R., & Juan-Císcar, A. (2009). The GERMANA database. In 10th International conference on document analysis and recognition (pp. 301–305). es_ES
dc.description.references Povey, D., Ghoshal, A., Boulianne, G., Burget, L., Glembek, O., Goel, N., Hannemann, M., Motlicek, P., Qian, Y., Schwarz, P., Silovsky, J., Stemmer, G., & Vesely, K. (2011). The Kaldi speech recognition toolkit. Technical report: IEEE signal processing society. es_ES
dc.description.references Puigcerver, J. (2017). Are multidimensional recurrent layers really necessary for handwritten text recognition? In Proceedings of the 14th IAPR international conference on document analysis and recognition (ICDAR) (Vol. 01, pp. 67–72). https://doi.org/10.1109/ICDAR.2017.20. es_ES
dc.description.references Puigcerver, J., Martin-Albo, D., & Villegas, M. (2016). Laia: A deep learning toolkit for HTR. es_ES
dc.description.references Sabir, E., Rawls, S., & Natarajan, P. (2017). Implicit language model in LSTM for OCR. In Proceedings of the 14th IAPR international conference on document analysis and recognition (ICDAR) (Vol. 7, pp. 27–31). IEEE. es_ES
dc.description.references Sanchez, J. A., Toselli, A. H., Romero, V., & Vidal, E. (2015). ICDAR 2015 competition HTRtS: Handwritten text recognition on the tranScriptorium dataset. In Proceedings of the 13th international conference on document analysis and recognition (ICDAR). es_ES
dc.description.references Shi, B., Bai, X., & Yao, C. (2016). An end-to-end trainable neural network for image-based sequence recognition and its application to scene text recognition. IEEE Transaction on Pattern Analysis and Machine Intelligence, 39(11), 2298–2304. es_ES
dc.description.references Slavik, P., & Govindaraju, V. (2001). Equivalence of Different Methods for Slant and Skew Corrections in Word Recognition Applications. IEEE Transaction on Pattern Analysis and Machine Intelligence, 23(3), 323–326. es_ES
dc.description.references Suen, C. Y., Nadal, C., Legault, R., Mai, T. A., & Lam, L. (1992). Computer recognition of unconstrained handwritten numerals. Special Issue of Proceedings of IEEE, 7(80), 1162–1180. es_ES
dc.description.references Toselli, A. H., Romero, V., & Vidal, E. (2007). Viterbi based alignment between text images and their transcripts. In Proceedings of the workshop on language technology for cultural heritage data (LaTeCH) (pp. 9–16). es_ES
dc.description.references Viard-Gaudin, C., Lallican, P. M., Knerr, S., & Binter, P. (1999). The IRESTE on/off (IRONOFF) dual handwriting database. In Proceedings of the fifth international conference on document analysis and recognition (ICDAR) (pp. 455–458). Bangalore, India. es_ES
dc.description.references Wilkinson, R., Geist, J., Janet, S., Grother, P., Burges, C., Creecy, R., Hammond, B., Hull, J., Larsen, N., Vogl, T., & Wilson, C. (1992). The first census optical character recognition systems conference. In #NISTIR 4912. The U.S. Bureau of Census and the National Institute of Standards and Technology, Gaithersburg, MD. es_ES


Este ítem aparece en la(s) siguiente(s) colección(ones)

Mostrar el registro sencillo del ítem