Graphic Symbol Recognition using Graph Based Signature and Bayesian Network Classifier

We present a new approach for recognition of complex graphic symbols in technical documents. Graphic symbol recognition is a well known challenge in the field of document image analysis and is at heart of most graphic recognition systems. Our method uses structural approach for symbol representation and statistical classifier for symbol recognition. In our system we represent symbols by their graph based signatures: a graphic symbol is vectorized and is converted to an attributed relational graph, which is used for computing a feature vector for the symbol. This signature corresponds to geometry and topology of the symbol. We learn a Bayesian network to encode joint probability distribution of symbol signatures and use it in a supervised learning scenario for graphic symbol recognition. We have evaluated our method on synthetically deformed and degraded images of pre-segmented 2D architectural and electronic symbols from GREC databases and have obtained encouraging recognition rates.Comment: 5 pages, 8 figures, Tenth International Conference on Document Analysis and Recognition (ICDAR), IEEE Computer Society, 2009, volume 10, 1325-132

Foothill: A Quasiconvex Regularization for Edge Computing of Deep Neural Networks

Deep neural networks (DNNs) have demonstrated success for many supervised learning tasks, ranging from voice recognition, object detection, to image classification. However, their increasing complexity might yield poor generalization error that make them hard to be deployed on edge devices. Quantization is an effective approach to compress DNNs in order to meet these constraints. Using a quasiconvex base function in order to construct a binary quantizer helps training binary neural networks (BNNs) and adding noise to the input data or using a concrete regularization function helps to improve generalization error. Here we introduce foothill function, an infinitely differentiable quasiconvex function. This regularizer is flexible enough to deform towards $L_1$ and $L_2$ penalties. Foothill can be used as a binary quantizer, as a regularizer, or as a loss. In particular, we show this regularizer reduces the accuracy gap between BNNs and their full-precision counterpart for image classification on ImageNet.Comment: Accepted in 16th International Conference of Image Analysis and Recognition (ICIAR 2019

Multi-task Layout Analysis of Handwritten Musical Scores

[EN] Document Layout Analysis (DLA) is a process that must be performed before attempting to recognize the content of handwritten musical scores by a modern automatic or semiautomatic system. DLA should provide the segmentation of the document image into semantically useful region types such as staff, lyrics, etc. In this paper we extend our previous work for DLA of handwritten text documents to also address complex handwritten music scores. This system is able to perform region segmentation, region classification and baseline detection in an integrated manner. Several experiments were performed in two different datasets in order to validate this approach and assess it in different scenarios. Results show high accuracy in such complex manuscripts and very competent computational time, which is a good indicator of the scalability of the method for very large collections.This work was partially supported by the Universitat Politecnica de Valencia under grant FPI-420II/899, a 2017-2018 Digital Humanities research grant of the BBVA Foundation for the project Carabela, the History Of Medieval Europe (HOME) project (Ref.: PCI2018-093122) and through the EU project READ (Horizon-2020 program, grant Ref. 674943). NVIDIA Corporation kindly donated the Titan X GPU used for this research.Quirós, L.; Toselli, AH.; Vidal, E. (2019). Multi-task Layout Analysis of Handwritten Musical Scores. Springer. 123-134. https://doi.org/10.1007/978-3-030-31321-0_11S123134Burgoyne, J.A., Ouyang, Y., Himmelman, T., Devaney, J., Pugin, L., Fujinaga, I.: Lyric extraction and recognition on digital images of early music sources. In: Proceedings of the 10th International Society for Music Information Retrieval Conference, vol. 10, pp. 723–727 (2009)Calvo-Zaragoza, J., Toselli, A.H., Vidal, E.: Probabilistic music-symbol spotting in handwritten scores. In: 16th International Conference on Frontiers in Handwriting Recognition (ICFHR), pp. 558–563, August 2018Calvo-Zaragoza, J., Zhang, K., Saleh, Z., Vigliensoni, G., Fujinaga, I.: Music document layout analysis through machine learning and human feedback. In: 14th IAPR International Conference on Document Analysis and Recognition (ICDAR), vol. 02, pp. 23–24, November 2017Calvo-Zaragoza, J., Castellanos, F.J., Vigliensoni, G., Fujinaga, I.: Deep neural networks for document processing of music score images. Appl. Sci. 8(5), 654 (2018). (2076-3417)Calvo-Zaragoza, J., Toselli, A.H., Vidal, E.: Handwritten music recognition for mensural notation: formulation, data and baseline results. In: 14th IAPR International Conference on Document Analysis and Recognition (ICDAR), vol. 1, pp. 1081–1086. IEEE (2017)Campos, V.B., Calvo-Zaragoza, J., Toselli, A.H., Ruiz, E.V.: Sheet music statistical layout analysis. In: 15th International Conference on Frontiers in Handwriting Recognition (ICFHR), pp. 313–318. IEEE (2016)Castellanos, F.J., Calvo-Zaragoza, J., Vigliensoni, G., Fujinaga, I.: Document analysis of music score images with selectional auto-encoders. In: 19th International Society for Music Information Retrieval Conference, pp. 256–263 (2018)Grüning, T., Labahn, R., Diem, M., Kleber, F., Fiel, S.: READ-BAD: a new dataset and evaluation scheme for baseline detection in archival documents. CoRR abs/1705.03311 (2017). http://arxiv.org/abs/1705.03311Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: 3rd International Conference on Learning Representations (ICLR) (2015)Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3431–3440 (2015)Quirós, L.: Multi-task handwritten document layout analysis. ArXiv e-prints, 1806.08852 (2018). https://arxiv.org/abs/1806.08852Quirós, L., Bosch, V., Serrano, L., Toselli, A.H., Vidal, E.: From HMMs to RNNs: computer-assisted transcription of a handwritten notarial records collection. In: 16th International Conference on Frontiers in Handwriting Recognition (ICFHR), pp. 116–121. IEEE, August 2018Rebelo, A., Fujinaga, I., Paszkiewicz, F., Marcal, A.R., Guedes, C., Cardoso, J.S.: Optical music recognition: state-of-the-art and open issues. Int. J. Multimed. Inf. Retrieval 1(3), 173–190 (2012)Sánchez, J.A., Romero, V., Toselli, A.H., Villegas, M., Vidal, E.: ICDAR2017 competition on handwritten text recognition on the READ dataset. In: 14th IAPR International Conference on Document Analysis and Recognition (ICDAR), vol. 1, pp. 1383–1388. IEEE (2017)Suzuki, S., et al.: Topological structural analysis of digitized binary images by border following. Comput. Vis. Graph. Image Process. 30(1), 32–46 (1985

Handwriting recognition by using deep learning to extract meaningful features

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