Fast Retrieval Algorithm Using EMD Lower and Upper Bounds and a Search Algorithm in multidimensional index

Comparison of images requires a distance metric that is sensitive to the spatial location of objects and features. The Earth Mover’s Distance was introduced in Computer Vision to better approach human perceptual similarities. Its computation, however, is too complex for usage in interactive multimedia database scenarios. We develop new upper bounding approximation techniques for the Earth Mover’s Distance which satisfy high quality criteria and fast computation. In order to enable efficient query processing in large databases, we propose an index structure LUBMTree (Lower and Upper Bounds MTree), based of using the lower and upper bounds for the EMD to improve the search time. Experiments show the performance of research in the  LUBMTree compared with those obtained by  the research in the MTree. Keywords : indexing, similarity, search, signature, metric EMD, MTree, MAM

EMD similarity measure and Metric Access Method using EMD lower bound

In this paper, we propose a method of indexing the color images, and an index structure of a large image database. The first method combines colors information of the pixels and their positions in the images. It also aims at reducing the size of signatures of each image, and consequently reducing the computation time between two signatures. The technique used for this purpose is the Kd-tree. The second contribution proposed in this paper is the LBMTree (Lower Bound MTree) structure, which is a method access metric. The LBMTree is an extension of the MTree structure. This structure uses the lower bounds of the EMD metric. Our goal is to improve the search time and maintain the effectiveness of the EMD metric in terms of accuracy. Experiments show the performance of research in the LBMTree compared with those obtained by the sequential scan, and the research in the MTree. In the other hand the performances of the proposed signature and of the EMD metric are measured, and results are shown

Multilingual character recognition dataset for Moroccan official documents

This article focuses on the construction of a dataset for multilingual character recognition in Moroccan official documents. The dataset covers languages such as Arabic, French, and Tamazight and are built programmatically to ensure data diversity. It consists of sub-datasets such as Uppercase alphabet (26 classes), Lowercase alphabet (26 classes), Digits (9 classes), Arabic (28 classes), Tifinagh letters (33 classes), Symbols (14 classes), and French special characters (16 classes). The dataset construction process involves collecting representative fonts and generating multiple character images using a Python script, presenting a comprehensive variety essential for robust recognition models. Moreover, this dataset contributes to the digitization of these diverse official documents and archival papers, essential for preserving cultural heritage and enabling advanced text recognition technologies. The need for this work arises from the advancements in character recognition techniques and the significance of large-scale annotated datasets. The proposed dataset contributes to the development of robust character recognition models for practical applications

Character Recognition Using Pre-Trained Models and Performance Variants Based on Datasets Size: A Survey

The most efficient and beneficial mechanism to the feature of extracting data from an image, has been the Convolutional Neural Network (CNN) and it is used in many fields (Optical character recognition, image classification, object recognition and Facial recognition etc.). In this papier, we studied the character classification problems, using pre-trained models based on Convolutional Neural Network (CNN), and how the performance can change the outcome of dataset that is given. For that, we have used five pre-trained models’ such as VGG16/19, ResNet, Xception et MobileNet. The experiment shows that Xception had the best performance rate compared to other models for all datasets, VGG16/19 performance rate are variants depend on dataset. However, Experiments shows that ResNet achieve the worst accuracy rate compared to other methods