280 research outputs found
String representations and distances in deep Convolutional Neural Networks for image classification
International audienceRecent advances in image classification mostly rely on the use of powerful local features combined with an adapted image representation. Although Convolutional Neural Network (CNN) features learned from ImageNet were shown to be generic and very efficient, they still lack of flexibility to take into account variations in the spatial layout of visual elements. In this paper, we investigate the use of structural representations on top of pre-trained CNN features to improve image classification. Images are represented as strings of CNN features. Similarities between such representations are computed using two new edit distance variants adapted to the image classification domain. Our algorithms have been implemented and tested on several challenging datasets, 15Scenes, Caltech101, Pas-cal VOC 2007 and MIT indoor. The results show that our idea of using structural string representations and distances clearly improves the classification performance over standard approaches based on CNN and SVM with linear kernel, as well as other recognized methods of the literature
Design of an Offline Handwriting Recognition System Tested on the Bangla and Korean Scripts
This dissertation presents a flexible and robust offline handwriting recognition system which is tested on the Bangla and Korean scripts. Offline handwriting recognition is one of the most challenging and yet to be solved problems in machine learning. While a few popular scripts (like Latin) have received a lot of attention, many other widely used scripts (like Bangla) have seen very little progress. Features such as connectedness and vowels structured as diacritics make it a challenging script to recognize. A simple and robust design for offline recognition is presented which not only works reliably, but also can be used for almost any alphabetic writing system. The framework has been rigorously tested for Bangla and demonstrated how it can be transformed to apply to other scripts through experiments on the Korean script whose two-dimensional arrangement of characters makes it a challenge to recognize.
The base of this design is a character spotting network which detects the location of different script elements (such as characters, diacritics) from an unsegmented word image. A transcript is formed from the detected classes based on their corresponding location information. This is the first reported lexicon-free offline recognition system for Bangla and achieves a Character Recognition Accuracy (CRA) of 94.8%. This is also one of the most flexible architectures ever presented. Recognition of Korean was achieved with a 91.2% CRA. Also, a powerful technique of autonomous tagging was developed which can drastically reduce the effort of preparing a dataset for any script. The combination of the character spotting method and the autonomous tagging brings the entire offline recognition problem very close to a singular solution.
Additionally, a database named the Boise State Bangla Handwriting Dataset was developed. This is one of the richest offline datasets currently available for Bangla and this has been made publicly accessible to accelerate the research progress. Many other tools were developed and experiments were conducted to more rigorously validate this framework by evaluating the method against external datasets (CMATERdb 1.1.1, Indic Word Dataset and REID2019: Early Indian Printed Documents). Offline handwriting recognition is an extremely promising technology and the outcome of this research moves the field significantly ahead
To Draw or Not to Draw: Recognizing Stroke-Hover Intent in Gesture-Free Bare-Hand Mid-Air Drawing Tasks
Over the past several decades, technological advancements have introduced new modes of communication
with the computers, introducing a shift from traditional mouse and keyboard interfaces.
While touch based interactions are abundantly being used today, latest developments in computer
vision, body tracking stereo cameras, and augmented and virtual reality have now enabled communicating
with the computers using spatial input in the physical 3D space. These techniques are now
being integrated into several design critical tasks like sketching, modeling, etc. through sophisticated
methodologies and use of specialized instrumented devices. One of the prime challenges in
design research is to make this spatial interaction with the computer as intuitive as possible for the
users.
Drawing curves in mid-air with fingers, is a fundamental task with applications to 3D sketching,
geometric modeling, handwriting recognition, and authentication. Sketching in general, is a
crucial mode for effective idea communication between designers. Mid-air curve input is typically
accomplished through instrumented controllers, specific hand postures, or pre-defined hand gestures,
in presence of depth and motion sensing cameras. The user may use any of these modalities
to express the intention to start or stop sketching. However, apart from suffering with issues like
lack of robustness, the use of such gestures, specific postures, or the necessity of instrumented
controllers for design specific tasks further result in an additional cognitive load on the user.
To address the problems associated with different mid-air curve input modalities, the presented
research discusses the design, development, and evaluation of data driven models for intent recognition
in non-instrumented, gesture-free, bare-hand mid-air drawing tasks.
The research is motivated by a behavioral study that demonstrates the need for such an approach
due to the lack of robustness and intuitiveness while using hand postures and instrumented
devices. The main objective is to study how users move during mid-air sketching, develop qualitative
insights regarding such movements, and consequently implement a computational approach to
determine when the user intends to draw in mid-air without the use of an explicit mechanism (such
as an instrumented controller or a specified hand-posture). By recording the user’s hand trajectory,
the idea is to simply classify this point as either hover or stroke. The resulting model allows for
the classification of points on the user’s spatial trajectory.
Drawing inspiration from the way users sketch in mid-air, this research first specifies the necessity
for an alternate approach for processing bare hand mid-air curves in a continuous fashion.
Further, this research presents a novel drawing intent recognition work flow for every recorded
drawing point, using three different approaches. We begin with recording mid-air drawing data
and developing a classification model based on the extracted geometric properties of the recorded
data. The main goal behind developing this model is to identify drawing intent from critical geometric
and temporal features. In the second approach, we explore the variations in prediction
quality of the model by improving the dimensionality of data used as mid-air curve input. Finally,
in the third approach, we seek to understand the drawing intention from mid-air curves using
sophisticated dimensionality reduction neural networks such as autoencoders. Finally, the broad
level implications of this research are discussed, with potential development areas in the design
and research of mid-air interactions
Computational Models for the Automatic Learning and Recognition of Irish Sign Language
This thesis presents a framework for the automatic recognition of Sign Language
sentences. In previous sign language recognition works, the issues of;
user independent recognition, movement epenthesis modeling and automatic
or weakly supervised training have not been fully addressed in a single recognition
framework. This work presents three main contributions in order to
address these issues.
The first contribution is a technique for user independent hand posture
recognition. We present a novel eigenspace Size Function feature which is
implemented to perform user independent recognition of sign language hand
postures.
The second contribution is a framework for the classification and spotting
of spatiotemporal gestures which appear in sign language. We propose a
Gesture Threshold Hidden Markov Model (GT-HMM) to classify gestures
and to identify movement epenthesis without the need for explicit epenthesis
training.
The third contribution is a framework to train the hand posture and spatiotemporal
models using only the weak supervision of sign language videos
and their corresponding text translations. This is achieved through our proposed
Multiple Instance Learning Density Matrix algorithm which automatically
extracts isolated signs from full sentences using the weak and noisy
supervision of text translations. The automatically extracted isolated samples
are then utilised to train our spatiotemporal gesture and hand posture
classifiers.
The work we present in this thesis is an important and significant contribution
to the area of natural sign language recognition as we propose a
robust framework for training a recognition system without the need for
manual labeling
Analyzing Handwritten and Transcribed Symbols in Disparate Corpora
Cuneiform tablets appertain to the oldest textual artifacts used for more than
three millennia and are comparable in amount and relevance
to texts written in Latin or ancient Greek.
These tablets are typically found in the Middle East and were
written by imprinting wedge-shaped impressions into wet clay.
Motivated by the increased demand for computerized analysis of documents within
the Digital Humanities, we develop the foundation for quantitative processing
of cuneiform script.
Using a 3D-Scanner to acquire a cuneiform tablet or manually creating line
tracings are two completely different representations of the same type of text
source. Each representation is typically processed with its own tool-set and
the textual analysis is therefore limited to a certain type of digital
representation. To homogenize these data source a unifying minimal wedge
feature description is introduced. It is extracted by
pattern matching and subsequent conflict resolution
as cuneiform is written densely with highly overlapping wedges.
Similarity metrics for cuneiform signs based on distinct
assumptions are presented. (i) An implicit model represents cuneiform signs
using undirected mathematical graphs and measures the similarity of
signs with graph kernels.
(ii) An explicit model approaches the problem of recognition by an optimal
assignment between the wedge configurations of two signs.
Further, methods for spotting cuneiform script are developed, combining
the feature descriptors for cuneiform wedges with prior work on
segmentation-free word spotting using part-structured models.
The ink-ball model is adapted by treating wedge feature descriptors as
individual parts.
The similarity metrics and the adapted spotting model are both evaluated
on a real-world dataset outperforming the state-of-the-art in
cuneiform sign similarity and spotting.
To prove the applicability of these methods for computational cuneiform
analysis, a novel approach is presented for mining frequent
constellations of wedges resulting in spatial n-grams. Furthermore,
a method for automatized transliteration of tablets is evaluated by
employing structured and sequential learning on a dataset of
parallel sentences. Finally, the conclusion
outlines how the presented methods enable the development of new tools
and computational analyses, which are objective and reproducible,
for quantitative processing of cuneiform script
Graph-Based Offline Signature Verification
Graphs provide a powerful representation formalism that offers great promise
to benefit tasks like handwritten signature verification. While most
state-of-the-art approaches to signature verification rely on fixed-size
representations, graphs are flexible in size and allow modeling local features
as well as the global structure of the handwriting. In this article, we present
two recent graph-based approaches to offline signature verification: keypoint
graphs with approximated graph edit distance and inkball models. We provide a
comprehensive description of the methods, propose improvements both in terms of
computational time and accuracy, and report experimental results for four
benchmark datasets. The proposed methods achieve top results for several
benchmarks, highlighting the potential of graph-based signature verification
Symbolic and Visual Retrieval of Mathematical Notation using Formula Graph Symbol Pair Matching and Structural Alignment
Large data collections containing millions of math formulae in different formats are available on-line. Retrieving math expressions from these collections is challenging. We propose a framework for retrieval of mathematical notation using symbol pairs extracted from visual and semantic representations of mathematical expressions on the symbolic domain for retrieval of text documents. We further adapt our model for retrieval of mathematical notation on images and lecture videos. Graph-based representations are used on each modality to describe math formulas. For symbolic formula retrieval, where the structure is known, we use symbol layout trees and operator trees. For image-based formula retrieval, since the structure is unknown we use a more general Line of Sight graph representation. Paths of these graphs define symbol pairs tuples that are used as the entries for our inverted index of mathematical notation. Our retrieval framework uses a three-stage approach with a fast selection of candidates as the first layer, a more detailed matching algorithm with similarity metric computation in the second stage, and finally when relevance assessments are available, we use an optional third layer with linear regression for estimation of relevance using multiple similarity scores for final re-ranking. Our model has been evaluated using large collections of documents, and preliminary results are presented for videos and cross-modal search. The proposed framework can be adapted for other domains like chemistry or technical diagrams where two visually similar elements from a collection are usually related to each other
Advances in Image Processing, Analysis and Recognition Technology
For many decades, researchers have been trying to make computers’ analysis of images as effective as the system of human vision is. For this purpose, many algorithms and systems have previously been created. The whole process covers various stages, including image processing, representation and recognition. The results of this work can be applied to many computer-assisted areas of everyday life. They improve particular activities and provide handy tools, which are sometimes only for entertainment, but quite often, they significantly increase our safety. In fact, the practical implementation of image processing algorithms is particularly wide. Moreover, the rapid growth of computational complexity and computer efficiency has allowed for the development of more sophisticated and effective algorithms and tools. Although significant progress has been made so far, many issues still remain, resulting in the need for the development of novel approaches
Mathematical Expression Recognition based on Probabilistic Grammars
[EN] Mathematical notation is well-known and used all over the
world. Humankind has evolved from simple methods representing
countings to current well-defined math notation able to account for
complex problems. Furthermore, mathematical expressions constitute a
universal language in scientific fields, and many information
resources containing mathematics have been created during the last
decades. However, in order to efficiently access all that information,
scientific documents have to be digitized or produced directly in
electronic formats.
Although most people is able to understand and produce mathematical
information, introducing math expressions into electronic devices
requires learning specific notations or using editors. Automatic
recognition of mathematical expressions aims at filling this gap
between the knowledge of a person and the input accepted by
computers. This way, printed documents containing math expressions
could be automatically digitized, and handwriting could be used for
direct input of math notation into electronic devices.
This thesis is devoted to develop an approach for mathematical
expression recognition. In this document we propose an approach for
recognizing any type of mathematical expression (printed or
handwritten) based on probabilistic grammars. In order to do so, we
develop the formal statistical framework such that derives several
probability distributions. Along the document, we deal with the
definition and estimation of all these probabilistic sources of
information. Finally, we define the parsing algorithm that globally
computes the most probable mathematical expression for a given input
according to the statistical framework.
An important point in this study is to provide objective performance
evaluation and report results using public data and standard
metrics. We inspected the problems of automatic evaluation in this
field and looked for the best solutions. We also report several
experiments using public databases and we participated in several
international competitions. Furthermore, we have released most of the
software developed in this thesis as open source.
We also explore some of the applications of mathematical expression
recognition. In addition to the direct applications of transcription
and digitization, we report two important proposals. First, we
developed mucaptcha, a method to tell humans and computers apart by
means of math handwriting input, which represents a novel application
of math expression recognition. Second, we tackled the problem of
layout analysis of structured documents using the statistical
framework developed in this thesis, because both are two-dimensional
problems that can be modeled with probabilistic grammars.
The approach developed in this thesis for mathematical expression
recognition has obtained good results at different levels. It has
produced several scientific publications in international conferences
and journals, and has been awarded in international competitions.[ES] La notación matemática es bien conocida y se utiliza en todo el
mundo. La humanidad ha evolucionado desde simples métodos para
representar cuentas hasta la notación formal actual capaz de modelar
problemas complejos. Además, las expresiones matemáticas constituyen
un idioma universal en el mundo científico, y se han creado muchos
recursos que contienen matemáticas durante las últimas décadas. Sin
embargo, para acceder de forma eficiente a toda esa información, los
documentos científicos han de ser digitalizados o producidos
directamente en formatos electrónicos.
Aunque la mayoría de personas es capaz de entender y producir
información matemática, introducir expresiones matemáticas en
dispositivos electrónicos requiere aprender notaciones especiales o
usar editores. El reconocimiento automático de expresiones matemáticas
tiene como objetivo llenar ese espacio existente entre el conocimiento
de una persona y la entrada que aceptan los ordenadores. De este modo,
documentos impresos que contienen fórmulas podrían digitalizarse
automáticamente, y la escritura se podría utilizar para introducir
directamente notación matemática en dispositivos electrónicos.
Esta tesis está centrada en desarrollar un método para reconocer
expresiones matemáticas. En este documento proponemos un método para
reconocer cualquier tipo de fórmula (impresa o manuscrita) basado en
gramáticas probabilísticas. Para ello, desarrollamos el marco
estadístico formal que deriva varias distribuciones de probabilidad. A
lo largo del documento, abordamos la definición y estimación de todas
estas fuentes de información probabilística. Finalmente, definimos el
algoritmo que, dada cierta entrada, calcula globalmente la expresión
matemática más probable de acuerdo al marco estadístico.
Un aspecto importante de este trabajo es proporcionar una evaluación
objetiva de los resultados y presentarlos usando datos públicos y
medidas estándar. Por ello, estudiamos los problemas de la evaluación
automática en este campo y buscamos las mejores soluciones. Asimismo,
presentamos diversos experimentos usando bases de datos públicas y
hemos participado en varias competiciones internacionales. Además,
hemos publicado como código abierto la mayoría del software
desarrollado en esta tesis.
También hemos explorado algunas de las aplicaciones del reconocimiento
de expresiones matemáticas. Además de las aplicaciones directas de
transcripción y digitalización, presentamos dos propuestas
importantes. En primer lugar, desarrollamos mucaptcha, un método para
discriminar entre humanos y ordenadores mediante la escritura de
expresiones matemáticas, el cual representa una novedosa aplicación
del reconocimiento de fórmulas. En segundo lugar, abordamos el
problema de detectar y segmentar la estructura de documentos
utilizando el marco estadístico formal desarrollado en esta tesis,
dado que ambos son problemas bidimensionales que pueden modelarse con
gramáticas probabilísticas.
El método desarrollado en esta tesis para reconocer expresiones
matemáticas ha obtenido buenos resultados a diferentes niveles. Este
trabajo ha producido varias publicaciones en conferencias
internacionales y revistas, y ha sido premiado en competiciones
internacionales.[CA] La notació matemàtica és ben coneguda i s'utilitza a tot el món. La
humanitat ha evolucionat des de simples mètodes per representar
comptes fins a la notació formal actual capaç de modelar
problemes complexos. A més, les expressions matemàtiques
constitueixen un idioma universal al món científic, i s'han creat
molts recursos que contenen matemàtiques durant les últimes
dècades. No obstant això, per accedir de forma eficient a tota
aquesta informació, els documents científics han de ser
digitalitzats o produïts directament en formats electrònics.
Encara que la majoria de persones és capaç d'entendre i produir
informació matemàtica, introduir expressions matemàtiques en
dispositius electrònics requereix aprendre notacions especials o usar
editors. El reconeixement automàtic d'expressions matemàtiques
té per objectiu omplir aquest espai existent entre el coneixement
d'una persona i l'entrada que accepten els ordinadors. D'aquesta
manera, documents impresos que contenen fórmules podrien
digitalitzar-se automàticament, i l'escriptura es podria utilitzar per
introduir directament notació matemàtica en dispositius electrònics.
Aquesta tesi està centrada en desenvolupar un mètode per reconèixer
expressions matemàtiques. En aquest document proposem un mètode per
reconèixer qualsevol tipus de fórmula (impresa o manuscrita) basat en
gramàtiques probabilístiques. Amb aquesta finalitat, desenvolupem el
marc estadístic formal que deriva diverses distribucions de
probabilitat. Al llarg del document, abordem la definició i estimació
de totes aquestes fonts d'informació probabilística. Finalment,
definim l'algorisme que, donada certa entrada, calcula globalment
l'expressió matemàtica més probable d'acord al marc estadístic.
Un aspecte important d'aquest treball és proporcionar una avaluació
objectiva dels resultats i presentar-los usant dades públiques i
mesures estàndard. Per això, estudiem els problemes de l'avaluació
automàtica en aquest camp i busquem les millors solucions. Així
mateix, presentem diversos experiments usant bases de dades públiques
i hem participat en diverses competicions internacionals. A més, hem
publicat com a codi obert la majoria del software desenvolupat en
aquesta tesi.
També hem explorat algunes de les aplicacions del reconeixement
d'expressions matemàtiques. A més de les aplicacions directes de
transcripció i digitalització, presentem dues propostes
importants. En primer lloc, desenvolupem mucaptcha, un mètode per
discriminar entre humans i ordinadors mitjançant l'escriptura
d'expressions matemàtiques, el qual representa una nova aplicació del
reconeixement de fórmules. En segon lloc, abordem el problema de
detectar i segmentar l'estructura de documents utilitzant el marc
estadístic formal desenvolupat en aquesta tesi, donat que ambdós són
problemes bidimensionals que poden modelar-se amb gramàtiques
probabilístiques.
El mètode desenvolupat en aquesta tesi per reconèixer expressions
matemàtiques ha obtingut bons resultats a diferents nivells. Aquest
treball ha produït diverses publicacions en conferències
internacionals i revistes, i ha sigut premiat en competicions
internacionals.Álvaro Muñoz, F. (2015). Mathematical Expression Recognition based on Probabilistic Grammars [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/51665TESI
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