6,263 research outputs found
Ein System für die Online-Erkennung handgeschriebener mathematischer Formeln
Title and Table of Contents
1.Introduction
2. Related Work
3. Preprocessing Techniques for On-Line Handwriting
4. Classification of On-Line Handwritten Symbols
5. Structural Analysis of Mathematical Expressions
6. An Editor for On-Line Handwritten Mathematical Expressions
7. Conclusion
BibliographyThis work presents a system for the recognition of on-line handwritten
mathematical formulas. The system consists of two main stages: Classification
of isolated on-line handwritten symbols and the analysis of spatial
relationships among them. We propose a system for the recognition of isolated
on-line handwritten characters which is based on support vector
classification. We also propose a suitable representation for strokes and
symbols which is used to improve the classification rates of the classifier.
Our experiments show that our classifier achieved better classification rates
in comparison to other popular classification techniques. This could be
accomplished by extensive preprocessing of the data and by parameter selection
for the support vector classification. We propose a new structural analysis
method for the recognition of on-line handwritten mathematical expressions
based on a minimum spanning tree construction and symbol dominance. Our method
addresses important layout problems frequently encountered in on-line
handwritten formula-recognition systems. Our method also aims to handle input
as naturally as possible, i.e. using the usual mathematical conventions,
without restrictions in the order the symbols are written. Our method handles
symbols with non-standard layout, like \sideset{^{*}_{*}}{^{*}_{*}}\prod, as
well as tabular layouts, e.g. matrices. Our system for the recognition of on-
line handwritten mathematical expressions is used in the Electronic Chalkboard
(E-Chalk), a multimedia system for distance-teaching.Die vorliegende Arbeit stellt ein System für die Online-Erkennung
handgeschriebener mathematischer Formeln vor. Das System besteht aus zwei
verschiedenen Komponenten, einem Klassifikator einzelner handgeschriebener
Online-Symbole und einem Analysator mathematischer Strukturen. Die Erkennung
der einzelnen Symbole erfolgt mittels Support-Vektor-Maschinen. Aus unserer
Experimenten ergab sich, dass unser Klassifikator gegenüber den klassischen
Techniken bessere Erkennungsraten erreichte. Diese Ergebnisse wurden durch
intensive Vorbearbeitung der Symbole und Suche optimaler Parameter ermöglicht.
Unsere Experimente lassen den Schluss zu, dass Support-Vektor-Maschinen den
Kompromiss zwischen Trainingszeit und Klassifikationsrate optimieren. In der
Arbeit wird eine neue Methode für die Online-Strukturanalyse handgeschriebener
mathematischer Ausdrücke besprochen, die sich auf der Aufbau eines minimalen
spannenden Baums und Symboldominanz basiert. Diese Technik ermöglicht eine
natürliche Eingabe der mathematischen Formeln, d.h., die Symbole und Formeln
werden ohne Beschränkungen nach der üblichen mathematischen Notation
geschrieben. Unsere Methode lässt sich einfach erweitern, um andere
mathematische Strukturen zu erkennen, z.B. Matrizen und andere ungewöhnliche
Strukturen, wie die in der LaTeX-Sprache definierte Struktur
sideset{^{*}_{*}}{^{*}_{*}} Unser Erkennungssystem wurde in der
Programmiersprache Java implementiert und ist das Standard-
Formelerkennungssystem des E-Kreide Systems
MathLet v3: recognizing handwritten mathematical expressions
This thesis presents MathLet v3 which is the third version of a system developed to recognize handwritten mathematical expressions. Previous versions were developed by Hakan Büyükbayrak and Mehmet Çelik. MathLet v3 implements two steps to recognize handwritten mathematical expressions; symbol recognition and parsing. In the symbol recognition step, two classifiers are combined. One of these classifiers uses online features while the other one uses offine features. Both classifiers return probability distributions over classes. In the parsing step, probability distributions are used to increase time performance of MathLet v3. Moreover, parallel programming is used in parsing phase. Special handling approach for mistaken symbols is also implemented in the parsing step. MathLet v3 has four applications and two of them can be accessed through the Web. Users write mathematical expressions or upload existing InkML les which contain mathematical expression and get recognition results for them through the Web by using these applications. MathLet has been participating in a competition named CROHME since 2011. The evaluation results of MathLet in CROHME show that the accuracy of MathLet has increased from 0.55% to 8.35% starting from 2011, although recognition task becomes more di cult each year. In addition to accuracy improvements, experiments made in order to measure the time performance of MathLet v3 show that MathLet v3 has become faster
Symbol detection in online handwritten graphics using Faster R-CNN
Symbol detection techniques in online handwritten graphics (e.g. diagrams and
mathematical expressions) consist of methods specifically designed for a single
graphic type. In this work, we evaluate the Faster R-CNN object detection
algorithm as a general method for detection of symbols in handwritten graphics.
We evaluate different configurations of the Faster R-CNN method, and point out
issues relative to the handwritten nature of the data. Considering the online
recognition context, we evaluate efficiency and accuracy trade-offs of using
Deep Neural Networks of different complexities as feature extractors. We
evaluate the method on publicly available flowchart and mathematical expression
(CROHME-2016) datasets. Results show that Faster R-CNN can be effectively used
on both datasets, enabling the possibility of developing general methods for
symbol detection, and furthermore, general graphic understanding methods that
could be built on top of the algorithm.Comment: Submitted to DAS-201
Multi-Scale Attention with Dense Encoder for Handwritten Mathematical Expression Recognition
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, we utilize
the attention based encoder-decoder model that recognizes mathematical
expression images from two-dimensional layouts to one-dimensional LaTeX
strings. 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 save the fine-grained details that will be
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
Math Search for the Masses: Multimodal Search Interfaces and Appearance-Based Retrieval
We summarize math search engines and search interfaces produced by the
Document and Pattern Recognition Lab in recent years, and in particular the min
math search interface and the Tangent search engine. Source code for both
systems are publicly available. "The Masses" refers to our emphasis on creating
systems for mathematical non-experts, who may be looking to define unfamiliar
notation, or browse documents based on the visual appearance of formulae rather
than their mathematical semantics.Comment: Paper for Invited Talk at 2015 Conference on Intelligent Computer
Mathematics (July, Washington DC
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