Optical character recognition (OCR) is the process of extracting handwritten or printed text from a scanned or printed image and converting it to a machine-readable form for further data processing, such as searching or editing. Automatic text extraction using OCR helps to digitize documents for improved productivity and accessibility and for preservation of historical documents. This paper provides a survey of the current state-of-the-art applications, techniques, and challenges in Arabic OCR. We present the existing methods for each step of the complete OCR process to identify the best-performing approach for improved results. This paper follows the keyword-search method for reviewing the articles related to Arabic OCR, including the backward and forward citations of the article. In addition to state-of-art techniques, this paper identifies research gaps and presents future directions for Arabic OCR.

Node localization is one of the most essential features of wireless sensor networks (WSNs). Vavarious localization algorithms exist for densely deployed 3-D wireless sensor networks. However, for a sparse 3-D network, range-based localization is still a challenging task because it is difficult to find sufficient anchor nodes and distance information among nodes in a sparse 3-D network. To mitigate the sparseness issues in 3-D sensor networks, we present a component-based localization method in this paper in which we split the entire network into small overlapping sub-networks called components and assign local coordinates to each component. Then, we merge these small components to make a globally coordinated system. With a meager anchor ratio, we localize the whole network. We define merging conditions according to the number of common nodes, actual measured distances among nodes, and the calculated distance based on the local coordinates of the nodes. We assess how well our proposed algorithm performs by conducting extensive simulations. The outcomes confirm that the proposed algorithm works comparatively better in a sparse 3-D sensor network than in a densely deployed 3-D sensor network. Our algorithm localizes more than 83% of nodes at a node degree of 10 having 5% anchor ratio; however, other algorithms localize only 18%-79% in the same scenario.

Graph summarization has become integral for managing and analyzing large-scale graphs in diverse real-world applications, including social networks, biological networks, and communication networks. Existing methods for graph summarization often face challenges, being either computationally expensive, limiting their applicability to large graphs, or lacking the incorporation of node attributes. In response, we introduce SsAG , an efficient and scalable lossy graph summarization method designed to preserve the essential structure of the original graph. SsAG computes a sparse representation (summary) of the input graph, accommodating graphs with node attributes. The summary is structured as a graph on supernodes (subsets of vertices of G ), where weighted superedges connect pairs of supernodes. The methodology focuses on constructing a summary graph with k supernodes, aiming to minimize the reconstruction error (the difference between the original graph and the graph reconstructed from the summary) while maximizing homogeneity with respect to the node attributes. The construction process involves iteratively merging pairs of nodes. To enhance computational efficiency, we derive a closed-form expression for efficiently computing the reconstruction error (RE) after merging a pair, enabling constant-time approximation of this score. We assign a weight to each supernode, quantifying their contribution to the score of pairs, and utilize a weighted sampling strategy to select the best pair for merging. Notably, a logarithmic-sized sample achieves a summary comparable in quality based on various measures. Additionally, we propose a sparsification step for the constructed summary, aiming to reduce storage costs to a specified target size with a marginal increase in RE. Empirical evaluations across diverse real-world graphs demonstrate that SsAG exhibits superior speed, being up to 17 × faster, while generating summaries of comparable quality. This work represents a significant advancement in the field, addressing computational challenges and showcasing the effectiveness of SsAG in graph summarization.