Background and Objectives: Signage is everywhere, and a robot should be able to take advantage of signs to help it localize (including Visual Place Recognition (VPR)) and map. Robust Text detection & recognition in the wild is challenging due to pose, irregular Text instances, illumination variations, viewpoint changes, and occlusion factors.Methods: This paper proposes an end-to-end scene Text spotting model that simultaneously outputs the Text string and bounding boxes. The proposed model leverages a pre-trained Vision Transformer based (ViT) architecture combined with a multi-task transformer-based Text detector more suitable for the VPR task. Our central contribution is introducing an end-to-end scene Text spotting framework to adequately capture the irregular and occluded Text regions in different challenging places. We first equip the ViT backbone using a masked autoencoder (MAE) to capture partially occluded characters to address the occlusion problem. Then, we use a multi-task prediction head for the proposed model to handle arbitrary shapes of Text instances with polygon bounding boxes.Results: The evaluation of the proposed architecture's performance for VPR involved conducting several experiments on the challenging Self-Collected Text Place (SCTP) benchmark dataset. The well-known evaluation metric, Precision-Recall, was employed to measure the performance of the proposed pipeline. The final model achieved the following performances, Recall = 0.93 and Precision = 0.8, upon testing on this benchmark.Conclusion: The initial experimental results show that the proposed model outperforms the state-of-the-art (SOTA) methods in comparison to the SCTP dataset, which confirms the robustness of the proposed end-to-end scene Text detection and recognition model.

Localizing Text regions in images taken from natural scenes is one of the challenging problems due to variations in font, size, color and orientation of Text. In this paper, we introduce a new concept so called Edge Color Signature for localizing Text regions in an image. This method is able to localize both Farsi and English Texts. In the proposed method first a pyramid using different scales of the input image is created. Then for each level of the pyramid an edge map is extracted. Afterward, several geometric features are employed to filter out the non-Text edges from the extracted edges. At this stage we describe an edge using colors of its neighboring pixels. We use the mean-Shift algorithm to obtain the color modes surrounding each edge pixel. Subsequently, the connected edge pixels with similar color signatures are clustered using Single-Linkage clustering algorithm to construct meaningful groups. Finally, each of the clusters is labeled as Text or non-Text using an MLP based cascade classifier. The proposed method has been evaluated on well-known ICDAR 2013 and our Farsi dataset, the result is very promising.

Video Text detection plays an important role in applications such as semantic-based video analysis, Text information retrieval, archiving and so on. In this paper, a Farsi/Arabic Text detection approach is proposed. First, a three-level resolution pyramid of input image is created. Then, with an appropriate edge detector, edges are extracted and then by using edges cross points, artificial corners are extracted. Artificial corner histogram analysis is done for rejecting non-Text corners. The discrete cosine transform (DCT) coefficients of input picture are extracted and Texture intensity picture is created by combining appropriate coefficients. With combining artificial corners image and Texture intensity image, a features vector is extracted and fed into support vector machine (SVM) classifier for detecting Text regions. Finally, with drawing normalized Texture intensity profiles, final verification is done and Text lines are separated from each other’s.

In this article, the writers make a distinction between two types of Text, open Text and closed Text. While the latter has a fixed and limited meaning, fixed signifiers and signifieds, with the signs always referring the reader to a fixed world, the former has the potential to be loaded with various meanings. To explicate the two types of Text and the factors that make a Text open, the writers draw on the views of great thinkers such as Rolan Barthes, Wolfgang Izer, Jacques Derrida and Carl Gustav Yung. The purpose of the article is to show how a familiarity with the western interpretations of Hermeneutics can affect our appreciation of Persian literature.

In this paper one of the main forms of Textualization will be represented; little known, in our opinion, first by foreign language specialists (linguists or literary) in Iran and marginalized, even absent, from the educational applications in language and literature courses in the Iranian universities.After an overview of the types of Texts, we will try to develop the idea that concerns us here, namely the study of argumentative type in two ways: firstly the epistemological concept of argumentation and then the organization of the argumentative Text. French: Le Texte argumentatif et la typologie de TextesResume Cet article aura comme but de representer l’une des formes principales de Textualisation, peu connue, a notre avis, d’abord par les specialistes des langues etrangeres (linguiste ou litteraire) en Iran et marginalisee, et meme absente, par consequent, des applications pedagogiques dans les cours de langue et litterature au milieu universitaire iranien.Apres un apercu general de la typologie des Textes, nous avons essaye de mettre au point l’idee qui nous preoccupe ici, a savoir l’etude du type argumentatif sur deux plans: d’abord plan epistemologique de la notion d’argumentation et ensuite plan organisationnel du Texte argumentatif.keywords: Typologie de Textes, Argumentation, Discours Argumentatif, Organisation de Texte, Categories de la Langue

One of the most important information security techniques is the hiding of information. Steganography is the art and science of hiding information in the cover of data (in the form of Text, image, video, or audio) such that it does not arise any suspicions, and is difficult or even impossible to discover. This paper presents a method for steganography in the form of Text which uses the methods of Text justifying in typing editors. The method presented in this paper is able to hide information better than some of previous algorithms in this field. This algorithm is resistant to various forms of attack such as visual, structural and statistical attacks. Another important capability of this method is that it can be used to send printed information.

In this paper, a new algorithm based on ESPRIT is proposed for the estimation of central angle and angular extension of Incoherently Distributed (ID) sources. The central angles are estimated using TLS-ESPRIT. The covariance matrix is approximated using a finite Taylor series expansion which leads to the formulation of covariance matrix in terms of central moments of the angular power distribution. The extension widths are estimated using the central moments of distribution. The algorithm can be used for sources with different angular distributions and has low computational cost.

Introduction: Application of ability and capabilities of modern educational technology is an opportunity to achieve effective and optimal learning. Also, localization is the consideration of indigenous knowledge in order to accumulate global knowledge of local needs and desires. In this research, with the native approach and according to the existing need, first in this research, the e-book, design, production and its impact on learning levels and retention were identified. Methods: The present study was carried out in combination with two qualitative and quantitative methods, the statistical population was undergraduate students at Allameh Tabatabaei University. The experimental group used a hyperText e-book. The data collection tool was a teacher test questionnaire whose validity was confirmed by specialists in the field of educational science. Using descriptive and inferential statistics (covariance and regression). Results: By comparing the data obtained in the control and test groups, the learning method with a hyperText e-book increased the learning and memory of students in the lessons of research methods in educational sciences in the experimental group (P value < 0. 05). The difference in mean of experimental and control groups is not significant at reminder level. However, the difference between the mean scores of the experimental and control groups in the level of understanding with regard to F at the level of less than 0. 05 was significant. Also, the difference between the mean scores on the level of work and the higher, according to the obtained T, is significant at the level of less than 0. 01. Conclusions: Therefore, it is possible to use the capabilities of the new technologies in the teaching and learning process in accordance with the standards in the design and production of media and the media to enhance learning.

The progress of communications over internet media such as social media and messengers has led to the production of large amount of Textual data. This kind of information contains a lot of valuable knowledge and can be used to improve the performance of other natural language processing (NLP) tasks. There are several ways to use such information, of which one is Text summarization. Summarizing Textual information can extract the main content of Text within a short time. In this paper, we propose an approach for extractive summarization on Persian Texts by using sentences embedding and a sparse coding framework. Most previous works focuses on Text’s sentences individually which may not consider the hidden structure patterns between them. In this paper, our proposed approach can consider the relations between the Text’s sentences by using three main criteria, namely coverage, diversity and sparsity, when selecting the summary sentences. By considering these criteria, we select sentences that can reconstruct the whole Text with least reconstruction error. The proposed approach is evaluated on Persian dataset Pasokh and achieved 10. 02% and 8. 65% improvement compared to the state-of-the-art methods in rouge-1 and rouge-2 f-scores, respectively. We show that considering semantic relations among the Text’s sentences can lead us to better sentence summarization.

The progress of communications over internet media such as social media and messengers has led to the production of large amount of Textual data. This kind of information contains a lot of valuable knowledge and can be used to improve the performance of other natural language processing (NLP) tasks. There are several ways to use such information, of which one is Text summarization. Summarizing Textual information can extract the main content of Text within a short time. In this paper, we propose an approach for extractive summarization on Persian Texts by using sentences embedding and a sparse coding framework. Most previous works focuses on Text’ s sentences individually which may not consider the hidden structure patterns between them. In this paper, our proposed approach can consider the relations between the Text’ s sentences by using three main criteria, namely coverage, diversity and sparsity, when selecting the summary sentences. By considering these criteria, we select sentences that can reconstruct the whole Text with least reconstruction error. The proposed approach is evaluated on Persian dataset Pasokh and achieved 10. 02% and 8. 65% improvement compared to the state-of-theart methods in rouge-1 and rouge-2 f-scores, respectively. We show that considering semantic relations among the Text’ s sentences can lead us to better sentence summarization.