Adaptive combinations of classifiers with application to on-line handwritten character recognition

Classifier combining is an effective way of improving classification performance. User adaptation is clearly another valid approach for improving performance in a user-dependent system, and even though adaptation is usually performed on the classifier level, also adaptive committees can be very effective. Adaptive committees have the distinct ability of performing adaptation without detailed knowledge of the classifiers. Adaptation can therefore be used even with classification systems that intrinsically are not suited for adaptation, whether that be due to lack of access to the workings of the classifier or simply a classification scheme not suitable for continuous learning. This thesis proposes methods for adaptive combination of classifiers in the setting of on-line handwritten character recognition. The focal part of the work introduces adaptive classifier combination schemes, of which the two most prominent ones are the Dynamically Expanding Context (DEC) committee and the Class-Confidence Critic Combining (CCCC) committee. Both have been shown to be capable of successful adaptation to the user in the task of on-line handwritten character recognition. Particularly the highly modular CCCC framework has shown impressive performance also in a doubly-adaptive setting of combining adaptive classifiers by using an adaptive committee. In support of this main topic of the thesis, some discussion on a methodology for deducing correct character labeling from user actions is presented. Proper labeling is paramount for effective adaptation, and deducing the labels from the user's actions is necessary to perform adaptation transparently to the user. In that way, the user does not need to give explicit feedback on the correctness of the recognition results. Also, an overview is presented of adaptive classification methods for single-classifier adaptation in handwritten character recognition developed at the Laboratory of Computer and Information Science of the Helsinki University of Technology, CIS-HCR. Classifiers based on the CIS-HCR system have been used in the adaptive committee experiments as both member classifiers and to provide a reference level. Finally, two distinct approaches for improving the performance of committee classifiers further are discussed. Firstly, methods for committee rejection are presented and evaluated. Secondly, measures of classifier diversity for classifier selection, based on the concept of diversity of errors, are presented and evaluated. The topic of this thesis hence covers three important aspects of pattern recognition: on-line adaptation, combining classifiers, and a practical evaluation setting of handwritten character recognition. A novel approach combining these three core ideas has been developed and is presented in the introductory text and the included publications. To reiterate, the main contributions of this thesis are: 1) introduction of novel adaptive committee classification methods, 2) introduction of novel methods for measuring classifier diversity, 3) presentation of some methods for implementing committee rejection, 4) discussion and introduction of a method for effective label deduction from on-line user actions, and as a side-product, 5) an overview of the CIS-HCR adaptive on-line handwritten character recognition system.Luokittimien yhdistäminen komitealuokittimella on tehokas keino luokitustarkkuuden parantamiseen. Laskentatehon jatkuva kasvu tekee myös useiden luokittimien yhtäaikaisesta käytöstä yhä varteenotettavamman vaihtoehdon. Järjestelmän adaptoituminen (mukautuminen) käyttäjään on toinen hyvä keino käyttäjäriippumattoman järjestelmän tarkkuuden parantantamiseksi. Vaikka adaptaatio yleensä toteutetaan luokittimen tasolla, myös adaptiiviset komitealuokittimet voivat olla hyvin tehokkaita. Adaptiiviset komiteat voivat adaptoitua ilman yksityiskohtaista tietoa jäsenluokittimista. Adaptaatiota voidaan näin käyttää myös luokittelujärjestelmissä, jotka eivät ole itsessään sopivia adaptaatioon. Adaptaatioon sopimattomuus voi johtua esimerkiksi siitä, että luokittimen totetutusta ei voida muuttaa, tai siitä, että käytetään luokittelumenetelmää, joka ei sovellu jatkuvaan oppimiseen. Tämä väitöskirja käsittelee menetelmiä luokittimien adaptiiviseen yhdistämiseen käyttäen sovelluskohteena käsinkirjoitettujen merkkien on-line-tunnistusta. Keskeisin osa työtä esittelee uusia adaptiivisia luokittimien yhdistämismenetelmiä, joista kaksi huomattavinta ovat Dynamically Expanding Context (DEC) -komitea sekä Class-Confidence Critic Combining (CCCC) -komitea. Molemmat näistä ovat osoittautuneet kykeneviksi tehokkaaseen käyttäjä-adaptaatioon käsinkirjoitettujen merkkien on-line-tunnistuksessa. Erityisesti hyvin modulaarisella CCCC järjestelmällä on saatu hyviä tuloksia myös kaksinkertaisesti adaptiivisessa asetelmassa, jossa yhdistetään adaptiivisia jäsenluokittimia adaptiivisen komitean avulla. Väitöskirjan pääteeman tukena esitetään myös malli ja käytännön esimerkki siitä, miten käyttäjän toimista merkeille voidaan päätellä oikeat luokat. Merkkien todellisen luokan onnistunut päättely on elintärkeää tehokkaalle adaptaatiolle. Jotta adaptaatio voitaisiin suorittaa käyttäjälle läpinäkyvästi, merkkien todelliset luokat on kyettävä päättelemään käyttäjän toimista. Tällä tavalla käyttäjän ei tarvitse antaa suoraa palautetta tunnistustuloksen oikeellisuudesta. Työssä esitetään myös yleiskatsaus Teknillisen korkeakoulun Informaatiotekniikan laboratoriossa kehitettyyn adaptiiviseen käsinkirjoitettujen merkkien tunnistusjärjestelmään. Tähän järjestelmään perustuvia luokittimia on käytetty adaptiivisten komitealuokittimien kokeissa sekä jäsenluokittimina että vertailutasona. Lopuksi esitellään kaksi erillistä menetelmää komitealuokittimen tarkkuuden edelleen parantamiseksi. Näistä ensimmäinen on joukko menetelmiä komitealuokittimen rejektion (hylkäyksen) toteuttamiseksi. Toinen esiteltävä menetelmä on käyttää luokittimien erilaisuuden mittoja jäsenluokittimien valintaa varten. Ehdotetut uudet erilaisuusmitat perustuvat käsitteeseen, jota kutsumme virheiden erilaisuudeksi. Väitöskirjan aihe kattaa kolme hahmontunnistuksen tärkeää osa-aluetta: online-adaptaation, luokittimien yhdistämisen ja käytännön sovellusalana käsinkirjoitettujen merkkien tunnistuksen. Näistä kolmesta lähtökohdasta on kehitetty uudenlainen synteesi, joka esitetään johdantotekstissä sekä liitteenä olevissa julkaisuissa. Tämän väitöskirjan oleellisimmat kontribuutiot ovat siten: 1) uusien adaptiivisten komitealuokittimien esittely, 2) uudenlaisten menetelmien esittely luokittimien erilaisuuden mittaamiseksi, 3) joidenkin komitearejektiomenetelmien esittely, 4) pohdinnan ja erään toteutustavan esittely syötettyjen merkkien todellisen luokan päättelemiseksi käyttäjän toimista, sekä sivutuotteena 5) kattava yleiskatsaus CIS-HCR adaptiiviseen on-line käsinkirjoitettujen merkkien tunnistusjärjestelmään.reviewe

Feature Extraction Methods for Character Recognition

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Advances in Character Recognition

This book presents advances in character recognition, and it consists of 12 chapters that cover wide range of topics on different aspects of character recognition. Hopefully, this book will serve as a reference source for academic research, for professionals working in the character recognition field and for all interested in the subject

Recent developments in the study of rapid human movements with the kinematic theory: Applications to handwriting and signature synthesis

International audienceHuman movement modeling can be of great interest for the design of pattern recognition systems relying on the understanding of the fine motor control (such as on-line handwriting recognition or signature verification) as well as for the development of intelligent systems involving in a way or another the processing of human movements. In this paper, we briefly list the different models that have been proposed in order to characterize the handwriting process and focus on a representation involving a vectorial summation of lognormal functions: the Sigma-lognormal model. Then, from a practical perspective, we describe a new stroke extraction algorithm suitable for the reverse engineering of handwriting signals. In the following section it is shown how the resulting representation can be used to study the writer and signer variability. We then report on two joint projects dealing with the automatic generation of synthetic specimens for the creation of large databases. The first application concerns the automatic generation of totally synthetic signature specimens for the training and evaluation of verification performances of automatic signature recognition systems. The second application deals with the synthesis of handwritten gestures for speeding up the learning process in customizable on-line recognition systems to be integrated in electronic pen pads

Document analysis at DFKI. - Part 1: Image analysis and text recognition

Document analysis is responsible for an essential progress in office automation. This paper is part of an overview about the combined research efforts in document analysis at the DFKI. Common to all document analysis projects is the global goal of providing a high level electronic representation of documents in terms of iconic, structural, textual, and semantic information. These symbolic document descriptions enable an "intelligent\u27; access to a document database. Currently there are three ongoing document analysis projects at DFKI: INCA, OMEGA, and PASCAL2000/PASCAL+. Though the projects pursue different goals in different application domains, they all share the same problems which have to be resolved with similar techniques. For that reason the activities in these projects are bundled to avoid redundant work. At DFKI we have divided the problem of document analysis into two main tasks, text recognition and text analysis, which themselves are divided into a set of subtasks. In a series of three research reports the work of the document analysis and office automation department at DFKI is presented. The first report discusses the problem of text recognition, the second that of text analysis. In a third report we describe our concept for a specialized document analysis knowledge representation language. The report in hand describes the activities dealing with the text recognition task. Text recognition covers the phase starting with capturing a document image up to identifying the written words. This comprises the following subtasks: preprocessing the pictorial information, segmenting into blocks, lines, words, and characters, classifying characters, and identifying the input words. For each subtask several competing solution algorithms, called specialists or knowledge sources, may exist. To efficiently control and organize these specialists an intelligent situation-based planning component is necessary, which is also described in this report. It should be mentioned that the planning component is also responsible to control the overall document analysis system instead of the text recognition phase onl

One-Class Classification: Taxonomy of Study and Review of Techniques

One-class classification (OCC) algorithms aim to build classification models when the negative class is either absent, poorly sampled or not well defined. This unique situation constrains the learning of efficient classifiers by defining class boundary just with the knowledge of positive class. The OCC problem has been considered and applied under many research themes, such as outlier/novelty detection and concept learning. In this paper we present a unified view of the general problem of OCC by presenting a taxonomy of study for OCC problems, which is based on the availability of training data, algorithms used and the application domains applied. We further delve into each of the categories of the proposed taxonomy and present a comprehensive literature review of the OCC algorithms, techniques and methodologies with a focus on their significance, limitations and applications. We conclude our paper by discussing some open research problems in the field of OCC and present our vision for future research.Comment: 24 pages + 11 pages of references, 8 figure

Character Recognition

Character recognition is one of the pattern recognition technologies that are most widely used in practical applications. This book presents recent advances that are relevant to character recognition, from technical topics such as image processing, feature extraction or classification, to new applications including human-computer interfaces. The goal of this book is to provide a reference source for academic research and for professionals working in the character recognition field