Fotini Simistira

Recognition of online handwritten mathematical formulas using probabilistic SVMs and stochastic context free grammars

Method to recognize spatial relations between mathematical objects in equations.A CYK-based algorithm on a 2D SCFG for recognizing mathematical expressions.Evaluation of the proposed methods on MathBrush and CROHME datasets. Although recognition of online handwritten text has reached a point of maturity, recognition of online handwritten mathematical expressions remains still a challenging problem. In this work we train a probabilistic SVM classifier to recognize spatial relations between two mathematical symbols or sub-expressions and then employ a CYK based algorithm to parse the mathematical expression in order to produce the respective MathML output. For the recognition of mathematical expressions we assume compliance with a stochastic context free grammar. It must be noted that in this work we make the assumption that the symbols that comprise the mathematical expression have been correctly recognized. We evaluate the recognition of spatial relation on the MathBrush database and the experimental results produce an overall mean error rate of 2.8%. MathML output is evaluated with the use of the datasets and evaluation tools of the CROHME2012 and CROHME2013 competitions. Experimental results give, at mathematical expression level, an accuracy of 78.70%, 65.78%, 56.37% and 50.22% for the Part-I, Part-II, Part-III and Part-IV on the respective test sets. Display Omitted

A Morphology Based Approach for Binarization of Handwritten Documents

Document image binarization is an initial though critical stage towards the recognition of the text components of a document. This paper describes an efficient method based on mathematical morphology for extracting text regions from degraded handwritten document images. The basic stages of our approach are: (a) top-hat-by-reconstruction to produce a filtered image with reasonable even background, (b) region growing starting from a set of seed points and attaching to each seed similar intensity neighboring pixels and (c) conditional extension of the initially detected text regions based on the values of the second derivative of the filtered image. The method was evaluated on the benchmarking dataset of the International Document Image Binarization Contest (DIBCO 2011) and show promising results.

Creating Ground Truth for Historical Manuscripts with Document Graphs and Scribbling Interaction

Ground truth is both - indispensable for training and evaluating document analysis methods, and yet very tedious to create manually. This especially holds true for complex historical manuscripts that exhibit challenging layouts with interfering and overlapping handwriting. In this paper, we propose a novel semi-automatic system to support layout annotations in such a scenario based on document graphs and a pen-based scribbling interaction. On the one hand, document graphs provide a sparse page representation that is already close to the desired ground truth and on the other hand, scribbling facilitates an efficient and convenient pen-based interaction with the graph. The performance of the system is demonstrated in the context of a newly introduced database of historical manuscripts with complex layouts.

A System for Recognition of On-Line Handwritten Mathematical Expressions

We present a system for recognizing online mathematical expressions (ME). Symbol recognition is based on a template elastic matching distance between pen direction features. The structural analysis of the ME is based on extracting the baseline of the ME and then classifying symbols into levels above and below the baseline. The symbols are then sequentially analyzed using six spatial relations and a respective 2d structure is processed to give the resulting MathML representation of the ME. The system was evaluated on the Competition on Recognition of Online Handwritten Mathematical Expressions (CROHME) 2011 datasets and demonstrates promising results.

Recognition of spatial relations in mathematical formulas

A critical issue in recognition of mathematical expressions is the identification of the spatial relations of the symbols or/and sub-expressions that comprise the entire mathematical formula. This paper addresses the problem of structural analysis of mathematical expressions by constructing appropriate feature vectors to represent the spatial affinity of the objects (mathematical symbols or sub-expressions) under examination and employing two popular machine learning techniques: (i) Support Vector Machines (SVM) and (ii) Artificial Neural Networks (ANN) to recognize the spatial relation between these objects. In order to evaluate the proposed techniques, we use Math Brush, a large publicly available dataset of mathematical expressions with annotated spatial relations, and a subset of spatial relations derived from the mathematical expressions the CROHME 2012 dataset. The experimental results give an overall mean error rate of 2.8% for the SVM and 3.4% for the ANN classifiers respectively, which are at par with other approaches evaluated on the same datasets.

Recognition of historical Greek polytonic scripts using LSTM networks

This paper reports on high-performance Optical Character Recognition (OCR) experiments using Long Short-Term Memory (LSTM) Networks for Greek polytonic script. Even though there are many Greek polytonic manuscripts, the digitization of such documents has not been widely applied, and very limited work has been done on the recognition of such scripts. We have collected a large number of diverse document pages of Greek polytonic scripts in a novel database, called Polyton-DB, containing 15; 689 textlines of synthetic and authentic printed scripts and performed baseline experiments using LSTM Networks. Evaluation results show that the character error rate obtained with LSTM varies from 5.51% to 14.68% (depending on the document) and is better than two well-known OCR engines, namely, Tesseract and ABBYY FineReader.

Recognition of Greek Polytonic on Historical Degraded Texts Using HMMs

Optical Character Recognition (OCR) of ancient Greek polytonic scripts is a challenging task due to the large number of character classes, resulting from variations of diacritical marks on the vowel letters. Classical OCR systems require a character segmentation phase, which in the case of Greek polytonic scripts is the main source of errors that finally affects the overall OCR performance. This paper suggests a character segmentation free HMM-based recognition system and compares its performance with other commercial, open source, and state-of-the art OCR systems. The evaluation has been carried out on a challenging novel dataset of Greek polytonic degraded texts and has shown that HMM-based OCR yields character and word level error rates of 8.61% and 25.30% respectively, which outperforms most of the available OCR systems and it is comparable with the performance of the state-of-the-art system based on LSTM Networks proposed recently.

iMuSciCA: Interactive Music Science Collaborative Activities for STEAM Learning

iMuSciCA supports mastery of core academic content on STEM subjects for secondary school students alongside with the development of their creativity and deeper learning skills, through engagement in music activities. To reach this goal, iMuSciCA introduces new methodologies and innovative technologies supporting active, discovery-based, collaborative, personalised, and more engaging learning. In particular, iMuSciCA delivers a suite of activity environments and tools on top of core enabling technologies integrated on a web-based platform. These include: a 3D environment for designing virtual musical instruments; advanced music generation and processing technologies to apply and interpret related physics and mathematics principles; gesture and pen-enabled multimodal interaction for music co-creation and performance; and 3D printing for realizing the virtual instruments. The educational deployment of the iMuSciCA workbench is built around a suite of interdisciplinary project/inquiry-based educational scenarios for STEAM, integrating innovative methods in teaching and learning. iMuSciCA is pilot-tested and evaluated in real learning contexts in secondary schools from three European countries. The chapter presents the innovative STEAM pedagogical framework, the implementation of the advanced activity environments and core enabling technologies, the design aspects of the educational scenarios and exemplar lesson plans, and the overall evaluation framework that is adopted in successive pilot testing in secondary schools of three European countries.