Canadian universities are challenged by the lack of graduating enough dentists to meet the future needs of the Canadian population. Foreign-trained dentists (FTD) represent a valuable resource to society and the economy. Dental programs have trained FTD for various reasons: public need for healthcare services, income generation for universities, and demand by FTD who desire to practice dentistry in Canada. Changes implemented by the National Dental Examining Board (NDEB) of Canada in 2000 have resulted in FTD no longer being able to gain Canadian dental licensure through a certification examination. FTD are now required to complete a two-year advanced placement qualifying or degree program at a Canadian dental school prior to receiving licensure. In 2003, the University of Manitoba launched a two-year International Dentist Degree Program (IDDP). In Part I of the study, 19 transcribed interviews of IDDP graduates between 2004-2008 were analyzed manually. Five qualitative themes emerged from the dataset. The themes are identified as: (1) isolation and physical relocation issues (i.e., from friends, family and their culture), (2) personal and professional demands of the program (i.e., maintaining home life with spouse and /or children as well as the professional demands of a dental student), (3) emotional stress associated with the program, (i.e., personal struggles and financial stresses), (4) re-learning a system (i.e., both cultural and professional), and (5) overall program satisfaction. In Part II of the study, the mean differences between the outcome variables (Clinical Grades, Didactic Grades, Final Grade Point Average, and NDEB Written and OSCE scores) were statistically analyzed between the 37 IDDP graduates and 246 regular-stream dental graduates from 2003-2011. Based on analysis of the data, the IDDP graduates performed better than the regular-stream dental graduates in all the variables. The mean scores in each of the outcome variables were higher than the regular-stream group, the only variable that was found to be statistically significant was observed in the NDEB Written scores (p>0.05).

Canadian universities are challenged by the lack of graduating enough dentists to meet the future needs of the Canadian population. Foreign-trained dentists (FTD) represent a valuable resource to society and the economy. Dental programs have trained FTD for various reasons: public need for healthcare services, income generation for universities, and demand by FTD who desire to practice dentistry in Canada. Changes implemented by the National Dental Examining Board (NDEB) of Canada in 2000 have resulted in FTD no longer being able to gain Canadian dental licensure through a certification examination. FTD are now required to complete a two-year advanced placement qualifying or degree program at a Canadian dental school prior to receiving licensure. In 2003, the University of Manitoba launched a two-year International Dentist Degree Program (IDDP). In Part I of the study, 19 transcribed interviews of IDDP graduates between 2004-2008 were analyzed manually. Five qualitative themes emerged from the dataset. The themes are identified as: (1) isolation and physical relocation issues (i.e., from friends, family and their culture), (2) personal and professional demands of the program (i.e., maintaining home life with spouse and /or children as well as the professional demands of a dental student), (3) emotional stress associated with the program, (i.e., personal struggles and financial stresses), (4) re-learning a system (i.e., both cultural and professional), and (5) overall program satisfaction. In Part II of the study, the mean differences between the outcome variables (Clinical Grades, Didactic Grades, Final Grade Point Average, and NDEB Written and OSCE scores) were statistically analyzed between the 37 IDDP graduates and 246 regular-stream dental graduates from 2003-2011. Based on analysis of the data, the IDDP graduates performed better than the regular-stream dental graduates in all the variables. The mean scores in each of the outcome variables were higher than the regular-stream group, the only variable that was found to be statistically significant was observed in the NDEB Written scores (p>0.05).

Annotation is essential in machine learning. Building an accurate object detec-tion model requires a large, diverse dataset, which poses challenges due to thetime-consuming nature of manual annotation. This thesis was made in collabora-tion with Project Ngulia, which aims at developing technical solutions to protectand monitor wild animals. A contribution of this work was to integrate an effi-cient semi-automatic image annotation tool within the Ngulia system, with theaim of streamlining the annotation process and improving the employed objectdetection models. Through research into available annotation tools, a custom toolwas deemed the most cost-effective and flexible option. It utilizes object detec-tion model predictions as annotation suggestions, improving the efficiency of theannotation process. The efficiency was evaluated through a user test, with partic-ipants achieving an average reduction of approximately 2 seconds in annotationspeed when utilizing suggestions. This reduction was supported as statisticallysignificant through a one-way ANOVA test. Additionally, it was investigated which images should be prioritized for an-notation in order to obtain the the most accurate predictions. Different samplingmethods were investigated and compared. The performance of the obtained mod-els remained relatively consistent, although with the even distribution methodat top. This indicate that the choice of sampling method may not substantiallyimpact the accuracy of the model, as the performance of the methods was rela-tively comparable. Moreover, different methods of selecting training data in there-training process was compared. The difference in performance was consider-ately small, likely due to the limited and balanced data pool. The experimentsdid however indicate that incorporating previously seen data with unseen datacould be beneficial, and that a reduced dataset can be sufficient. However, furtherinvestigation is required to fully understand the extent of these benefits.

Le nombre important de campagnes de numérisation mises en place ces deux dernières décennies a entraîné une effervescence scientifique ayant mené à la création de nombreuses méthodes pour traiter et/ou analyser ces images de documents (reconnaissance d’écriture, analyse de la structure de documents, détection/indexation et recherche d’éléments graphiques, etc.). Un bon nombre de ces approches est basé sur un apprentissage (supervisé, semi supervisé ou non supervisé). Afin de pouvoir entraîner les algorithmes correspondants et en comparer les performances, la communauté scientifique a un fort besoin de bases publiques d’images de documents avec la vérité-terrain correspondante, et suffisamment exhaustive pour contenir des exemples représentatifs du contenu des documents à traiter ou analyser. La constitution de bases d’images de documents réels nécessite d’annoter les données (constituer la vérité terrain). Les performances des approches récentes d’annotation automatique étant très liées à la qualité et à l’exhaustivité des données d’apprentissage, ce processus d’annotation reste très largement manuel. Ce processus peut s’avérer complexe, subjectif et fastidieux. Afin de tenter de pallier à ces difficultés, plusieurs initiatives de crowdsourcing ont vu le jour ces dernières années, certaines sous la forme de jeux pour les rendre plus attractives. Si ce type d’initiatives permet effectivement de réduire le coût et la subjectivité des annotations, reste un certain nombre de difficultés techniques difficiles à résoudre de manière complètement automatique, par exemple l’alignement de la transcription et des lignes de texte automatiquement extraites des images. Une alternative à la création systématique de bases d’images de documents étiquetées manuellement a été imaginée dès le début des années 90. Cette alternative consiste à générer des images semi-synthétiques imitant les images réelles. La génération d’images de documents semi-synthétiques permet de constituer rapidement un volume de données important et varié, répondant ainsi aux besoins de la communauté pour l’apprentissage et l’évaluation de performances de leurs algorithmes. Dans la cadre du projet DIGIDOC (Document Image diGitisation with Interactive DescriptiOn Capability) financé par l’ANR (Agence Nationale de la Recherche), nous avons mené des travaux de recherche relatifs à la génération d’images de documents anciens semi-synthétiques. Le premier apport majeur de nos travaux réside dans la création de plusieurs modèles de dégradation permettant de reproduire de manière synthétique des déformations couramment rencontrées dans les images de documents anciens (dégradation de l’encre, déformation du papier, apparition de la transparence, etc.). Le second apport majeur de ces travaux de recherche est la mise en place de plusieurs bases d’images semi-synthétiques utilisées dans des campagnes de test (compétition ICDAR2013, GREC2013) ou pour améliorer par ré-apprentissage les résultats de méthodes de reconnaissance de caractères, de segmentation ou de binarisation. Ces travaux ont abouti sur plusieurs collaborations nationales et internationales, qui se sont soldées en particulier par plusieurs publications communes. Notre but est de valider de manière la plus objective possible, et en collaboration avec la communauté scientifique concernée, l’intérêt des images de documents anciens semi-synthétiques générées pour l’évaluation de performances et le ré-apprentissage. / In the last two decades, the increase in document image digitization projects results in scientific effervescence for conceiving document image processing and analysis algorithms (handwritten recognition, structure document analysis, spotting and indexing / retrieval graphical elements, etc.). A number of successful algorithms are based on learning (supervised, semi-supervised or unsupervised). In order to train such algorithms and to compare their performances, the scientific community on document image analysis needs many publicly available annotated document image databases. Their contents must be exhaustive enough to be representative of the possible variations in the documents to process / analyze. To create real document image databases, one needs an automatic or a manual annotation process. The performance of an automatic annotation process is proportional to the quality and completeness of these databases, and therefore annotation remains largely manual. Regarding the manual process, it is complicated, subjective, and tedious. To overcome such difficulties, several crowd-sourcing initiatives have been proposed, and some of them being modelled as a game to be more attractive. Such processes reduce significantly the price andsubjectivity of annotation, but difficulties still exist. For example, transcription and textline alignment have to be carried out manually. Since the 1990s, alternative document image generation approaches have been proposed including in generating semi-synthetic document images mimicking real ones. Semi-synthetic document image generation allows creating rapidly and cheaply benchmarking databases for evaluating the performances and trainingdocument processing and analysis algorithms. In the context of the project DIGIDOC (Document Image diGitisation with Interactive DescriptiOn Capability) funded by ANR (Agence Nationale de la Recherche), we focus on semi-synthetic document image generation adapted to ancient documents. First, we investigate new degradation models or adapt existing degradation models to ancient documents such as bleed-through model, distortion model, character degradation model, etc. Second, we apply such degradation models to generate semi-synthetic document image databases for performance evaluation (e.g the competition ICDAR2013, GREC2013) or for performance improvement (by re-training a handwritten recognition system, a segmentation system, and a binarisation system). This research work raises many collaboration opportunities with other researchers to share our experimental results with our scientific community. This collaborative work also helps us to validate our degradation models and to prove the efficiency of semi-synthetic document images for performance evaluation and re-training.