Author
Francisco Álvaro
Bio: Francisco Álvaro is an academic researcher from Polytechnic University of Valencia. The author has contributed to research in topics: Handwriting recognition & Parsing. The author has an hindex of 8, co-authored 15 publications receiving 314 citations.
Papers
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TL;DR: In this article, a formal model for the recognition of on-line handwritten mathematical expressions using 2D stochastic context-free grammars and hidden Markov models is described.
107 citations
TL;DR: The statistical framework of a model based on two-dimensional grammars and its associated parsing algorithm is defined and a system that implements this approach is developed and results on the large public dataset of the CROHME international competition are reported.
69 citations
18 Sep 2011
TL;DR: A statistical framework based on two-dimensional stochastic context-free grammars has been defined that allows to jointly tackle the segmentation, symbol recognition and structural analysis of a mathematical expression by computing its most probable parsing.
Abstract: In this work, a system for recognition of printed mathematical expressions has been developed. Hence, a statistical framework based on two-dimensional stochastic context-free grammars has been defined. This formal framework allows to jointly tackle the segmentation, symbol recognition and structural analysis of a mathematical expression by computing its most probable parsing. In order to test this approach a reproducible and comparable experiment has been carried out over a large publicly available (InftyCDB-1) database. Results are reported using a well-defined global dissimilitude measure. Experimental results show that this technique is able to properly recognize mathematical expressions, and that the structural information improves the symbol recognition step.
48 citations
10 Sep 2013
TL;DR: This work improves existing geometric features based on bounding boxes and center points, and proposes a novel feature set for layout classification, using polar histograms computed over points in handwritten strokes, which provide a natural representation with results comparable to those for the geometric features.
Abstract: We consider the difficult problem of classifying spatial relationships between symbols and subexpressions in handwritten mathematical expressions. We first improve existing geometric features based on bounding boxes and center points, normalizing them using the distance between the centers of the two symbols or subexpressions in question. We then propose a novel feature set for layout classification, using polar histograms computed over points in handwritten strokes. A series of experiments are presented in which a Support Vector Machine is used with these new features to classify spatial relationships of five types in the MathBrush corpus (horizontal, superscript, subscript, below, and inside (e.g. in a square root)). The normalized geometric features provide an improvement over previously published results, while the shape-based features provide a natural representation with results comparable to those for the geometric features. Combining the features produced a very small improvement in accuracy.
35 citations
24 Aug 2014
TL;DR: The performance of several well-known offline features for handwritten math symbol recognition are assessed and a novel set of features based on polar histograms and the vertical repositioning method for feature extraction is tested.
Abstract: In mathematical expression recognition, symbol classification is a crucial step. Numerous approaches for recognizing handwritten math symbols have been published, but most of them are either an online approach or a hybrid approach. There is an absence of a study focused on offline features for handwritten math symbol recognition. Furthermore, many papers provide results difficult to compare. In this paper we assess the performance of several well-known offline features for this task. We also test a novel set of features based on polar histograms and the vertical repositioning method for feature extraction. Finally, we report and analyze the results of several experiments using recurrent neural networks on a large public database of online handwritten math expressions. The combination of online and offline features significantly improved the recognition rate.
34 citations
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TL;DR: A comprehensive review of LSTM’s formulation and training, relevant applications reported in the literature and code resources implementing this model for a toy example are presented.
Abstract: Long short-term memory (LSTM) has transformed both machine learning and neurocomputing fields. According to several online sources, this model has improved Google’s speech recognition, greatly improved machine translations on Google Translate, and the answers of Amazon’s Alexa. This neural system is also employed by Facebook, reaching over 4 billion LSTM-based translations per day as of 2017. Interestingly, recurrent neural networks had shown a rather discrete performance until LSTM showed up. One reason for the success of this recurrent network lies in its ability to handle the exploding/vanishing gradient problem, which stands as a difficult issue to be circumvented when training recurrent or very deep neural networks. In this paper, we present a comprehensive review that covers LSTM’s formulation and training, relevant applications reported in the literature and code resources implementing this model for a toy example.
412 citations
TL;DR: Watch, Attend and Parse (WAP), a novel end-to-end approach based on neural network that learns to recognize HMEs in a two-dimensional layout and outputs them as one-dimensional character sequences in LaTeX format, significantly outperformed the state-of-the-art method.
182 citations
TL;DR: This review article serves the purpose of presenting state of the art results and techniques on OCR and also provide research directions by highlighting research gaps.
Abstract: Given the ubiquity of handwritten documents in human transactions, Optical Character Recognition (OCR) of documents have invaluable practical worth. Optical character recognition is a science that enables to translate various types of documents or images into analyzable, editable and searchable data. During last decade, researchers have used artificial intelligence/machine learning tools to automatically analyze handwritten and printed documents in order to convert them into electronic format. The objective of this review paper is to summarize research that has been conducted on character recognition of handwritten documents and to provide research directions. In this Systematic Literature Review (SLR) we collected, synthesized and analyzed research articles on the topic of handwritten OCR (and closely related topics) which were published between year 2000 to 2019. We followed widely used electronic databases by following pre-defined review protocol. Articles were searched using keywords, forward reference searching and backward reference searching in order to search all the articles related to the topic. After carefully following study selection process 176 articles were selected for this SLR. This review article serves the purpose of presenting state of the art results and techniques on OCR and also provide research directions by highlighting research gaps.
139 citations
01 Jan 2016
TL;DR: Thank you for downloading exploring artificial intelligence in the new millennium, instead of reading a good book with a cup of coffee in the afternoon, instead they are facing with some infectious virus inside their computer.
Abstract: Thank you for downloading exploring artificial intelligence in the new millennium. Maybe you have knowledge that, people have search hundreds times for their chosen novels like this exploring artificial intelligence in the new millennium, but end up in infectious downloads. Rather than reading a good book with a cup of coffee in the afternoon, instead they are facing with some infectious virus inside their computer.
137 citations
01 Aug 2018
TL;DR: In this paper, the encoder-decoder model was improved by employing densely connected convolutional networks as they can strengthen feature extraction and facilitate gradient propagation especially on a small training set, and a multi-scale attention model was employed to deal with the recognition of math symbols in different scales and restore the fine-grained details dropped by pooling operations.
Abstract: Handwritten mathematical expression recognition is a challenging problem due to the complicated two-dimensional structures, ambiguous handwriting input and variant scales of handwritten math symbols. To settle this problem, recently we propose the attention based encoder-decoder model that recognizes mathematical expression images from two-dimensional layouts to one-dimensional LaTeX strings. In this study, we improve the encoder by employing densely connected convolutional networks as they can strengthen feature extraction and facilitate gradient propagation especially on a small training set. We also present a novel multi-scale attention model which is employed to deal with the recognition of math symbols in different scales and restore the fine-grained details dropped by pooling operations. Validated on the CROHME competition task, the proposed method significantly outperforms the state-of-the-art methods with an expression recognition accuracy of 52.8% on CROHME 2014 and 50.1% on CROHME 2016, by only using the official training dataset.
115 citations