Research report on Bengla OCR training and testing methods

Includes bibliographical references (page 6-7).In this paper we present the training and recognition mechanism of a Hidden Markov Model (HMM) based multi-font Optical Character Recognition (OCR) system for Bengali character. In our approach, the central idea is to separate the HMM model for each segmented character or word. The system uses HTK toolkit for data preparation, model training and recognition. The Features of each trained character are calculated by applying the Discrete Cosine Transform (DCT) to each pixel value of the character image where the image is divided into several frames according to its size. The extracted features of each frame are used as discrete probability distributions which will be given as input parameters to each HMM model. In the case of recognition, a model for each separated character or word is built up using the same approach. This model is given to the HTK toolkit to perform the recognition using the Viterbi Decoding method. The experimental results show significant performance over models using neural network based training and recognition systems.Md. Abul Hasna

Does parliamentary development assistance matter? : an examination of the aid effectiveness in parliamentary oversight

As the central institution of democracy, parliaments embody the will of the people in government, and carry all their expectations that democracy will be truly responsive to their needs and help solve the most pressing problems that confront them in their daily lives. With more countries preferring democracy over other systems of government, parliaments and other legislative assemblies have become increasingly pertinent. In broad terms, everybody agrees on what the functions of a parliament are. These bodies make laws, hold the executive branch accountable, and represent citizen interests. Achieving democratic governance, therefore, requires the existence of a strong, effective and efficient parliament or legislative body.The thesis highlights the specific challenges parliaments face in performing such crucial role, particularly fulfilling its oversight mandate. This becomes more daunting where parliaments and legislative bodies are not considered credible or trustworthy institutions, or do not enjoy the support from executives. Many parliaments and the likes are chronically under-staffed and ill-informed. More often than not, they are sorely under-resourced and vital research, legislative drafting, and other capacities are often in short supply. That is why parliaments in most emerging democracies look to the international community for support, as do civil society organisations. Support to ‘parliaments and parliamentarians’ is a relatively new, but rapidly growing area of cooperation provided by different donors and international organisations to representative institutions.The thesis attempts to do primarily three things: firstly, it offers a framework that links a set of specific democratic and aid effectiveness principles to the institutional means by which democratic and parliamentary institutions are supported. As part of this, it provides a compilation of practices whereby parliaments seek to put these principles into effect with international assistance, known as Parliamentary Development Assistance (PDA). In fact, a clear and consistent set of lessons and recommendations about how international development and parliamentary actors can improve their assistance has emerged over the past two decades [‘Lessons learned’ and ‘Good practices’]. Secondly, it explores whether this results into any distinct approach to parliamentary oversight. It examines whether the PDA demonstrated capacity to promote substantial changes to the parliamentary oversight mechanisms in order to address the challenges of corruption better. Thirdly, the thesis sheds light on the nexus between technical support and political environment – often expressed through political will - and, political economy analysis, ignored too long in the name of ‘neutral technical support’. The thesis reinforces that political behaviour and culture cannot be changed quickly. This requires long term engagements, and, calls for enduring commitment and collaboration.The thesis identifies distinct gaps in the literature of studies of the impact of parliamentary development assistance (PDA). It seeks to consider the work of international organisations, research institutions, and donors with the parliaments of different countries and developmental situations in terms of their capacity to make a difference to the strengthening of parliamentary development and oversight work. Donors - development partners and international actors will need to make a durable commitment to programmes based on robust local and political analysis, and reduce the number of short-term interventions, quick fixes, and small-scale projects

Joint Color-Spatial-Directional clustering and Region Merging (JCSD-RM) for unsupervised RGB-D image segmentation

International audienceRecent advances in depth imaging sensors provide easy access to the synchronized depth with color, called RGB-D image. In this paper, we propose an unsupervised method for indoor RGB-D image segmentation and analysis. We consider a statistical image generation model based on the color and geometry of the scene. Our method consists of a joint color-spatial-directional clustering method followed by a statistical planar region merging method. We evaluate our method on the NYU depth database and compare it with existing unsupervised RGB-D segmentation methods. Results show that, it is comparable with the state of the art methods and it needs less computation time. Moreover, it opens interesting perspectives to fuse color and geometry in an unsupervised manner

Classification non supervisée d’images 3D et extension à la segmentation exploitant les informations de couleur et de profondeur

Access to the 3D images at a reasonable frame rate is widespread now, thanks to the recent advances in low cost depth sensors as well as the efficient methods to compute 3D from 2D images. As a consequence, it is highly demanding to enhance the capability of existing computer vision applications by incorporating 3D information. Indeed, it has been demonstrated in numerous researches that the accuracy of different tasks increases by including 3D information as an additional feature. However, for the task of indoor scene analysis and segmentation, it remains several important issues, such as: (a) how the 3D information itself can be exploited? and (b) what is the best way to fuse color and 3D in an unsupervised manner? In this thesis, we address these issues and propose novel unsupervised methods for 3D image clustering and joint color and depth image segmentation. To this aim, we consider image normals as the prominent feature from 3D image and cluster them with methods based on finite statistical mixture models. We consider Bregman Soft Clustering method to ensure computationally efficient clustering. Moreover, we exploit several probability distributions from directional statistics, such as the von Mises-Fisher distribution and the Watson distribution. By combining these, we propose novel Model Based Clustering methods. We empirically validate these methods using synthetic data and then demonstrate their application for 3D/depth image analysis. Afterward, we extend these methods to segment synchronized 3D and color image, also called RGB-D image. To this aim, first we propose a statistical image generation model for RGB-D image. Then, we propose novel RGB-D segmentation method using a joint color-spatial-axial clustering and a statistical planar region merging method. Results show that, the proposed method is comparable with the state of the art methods and requires less computation time. Moreover, it opens interesting perspectives to fuse color and geometry in an unsupervised manner. We believe that the methods proposed in this thesis are equally applicable and extendable for clustering different types of data, such as speech, gene expressions, etc. Moreover, they can be used for complex tasks, such as joint image-speech data analysisL'accès aux séquences d'images 3D s'est aujourd'hui démocratisé, grâce aux récentes avancées dans le développement des capteurs de profondeur ainsi que des méthodes permettant de manipuler des informations 3D à partir d'images 2D. De ce fait, il y a une attente importante de la part de la communauté scientifique de la vision par ordinateur dans l'intégration de l'information 3D. En effet, des travaux de recherche ont montré que les performances de certaines applications pouvaient être améliorées en intégrant l'information 3D. Cependant, il reste des problèmes à résoudre pour l'analyse et la segmentation de scènes intérieures comme (a) comment l'information 3D peut-elle être exploitée au mieux ? et (b) quelle est la meilleure manière de prendre en compte de manière conjointe les informations couleur et 3D ? Nous abordons ces deux questions dans cette thèse et nous proposons de nouvelles méthodes non supervisées pour la classification d'images 3D et la segmentation prenant en compte de manière conjointe les informations de couleur et de profondeur. A cet effet, nous formulons l'hypothèse que les normales aux surfaces dans les images 3D sont des éléments à prendre en compte pour leur analyse, et leurs distributions sont modélisables à l'aide de lois de mélange. Nous utilisons la méthode dite « Bregman Soft Clustering » afin d'être efficace d'un point de vue calculatoire. De plus, nous étudions plusieurs lois de probabilités permettant de modéliser les distributions de directions : la loi de von Mises-Fisher et la loi de Watson. Les méthodes de classification « basées modèles » proposées sont ensuite validées en utilisant des données de synthèse puis nous montrons leur intérêt pour l'analyse des images 3D (ou de profondeur). Une nouvelle méthode de segmentation d'images couleur et profondeur, appelées aussi images RGB-D, exploitant conjointement la couleur, la position 3D, et la normale locale est alors développée par extension des précédentes méthodes et en introduisant une méthode statistique de fusion de régions « planes » à l'aide d'un graphe. Les résultats montrent que la méthode proposée donne des résultats au moins comparables aux méthodes de l'état de l'art tout en demandant moins de temps de calcul. De plus, elle ouvre des perspectives nouvelles pour la fusion non supervisée des informations de couleur et de géométrie. Nous sommes convaincus que les méthodes proposées dans cette thèse pourront être utilisées pour la classification d'autres types de données comme la parole, les données d'expression en génétique, etc. Elles devraient aussi permettre la réalisation de tâches complexes comme l'analyse conjointe de données contenant des images et de la parol