Text Regions Research Articles

Evaluating the visual literacy of the Kuwaiti government’s Arabic infographics during the COVID-19 pandemic: an automated visual analysis and AI approach

PurposeWith infographics emerging as key communication tools on social media platforms, this study explores the visual literacy of governments in creating effective infographics, especially during crises. Using the Kuwaiti government during the COVID-19 pandemic as a case study, the investigation evaluates the government’s visual competency and strategy in its infographics on X.Design/methodology/approachFor competence, AI-based techniques were employed to analyze the proportion of text region size to total infographic size, word count per infographic and the most prominent colors used. Regarding strategy, the study utilized the crisis and emergency risk communication (CERC) model as a framework to examine how the Kuwaiti government integrated crisis communication response strategies into infographics.FindingsWhen communicating complex messages, the government resorted to text-heavy infographics instead of creative visualizations, casting doubt on its visual competence. The inconsistent use of colors further undermined a recognizable visual identity. Regarding strategy, infographics on crisis updates were most frequent, supporting CERC’s emphasis on reducing uncertainty. Yet, prioritizing bolstering strategies above empathy and action steps goes against the advice of existing literature.Originality/valueWhile crisis communication research is widely based on textual analysis, this study extends the literature by examining visuals, specifically infographics. Additionally, focusing on Arabic infographics from Kuwait, the study expands the crisis communication literature, which has mainly concentrated on Western countries and the English language. Given the lack of consensus on the best methods to measure visual literacy, this study’s AI approaches contribute to the literature.Peer reviewThe peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-03-2024-0172

Text Detection on Industrial Barrel Label with Convolutional Attention and Dual‐Branch Feature Network

Industrial barrel labels generally have low visual contrast, uneven lighting, and cluttered background, making it challenging to accurately locate text regions. This paper proposes a text detection network to solve the inaccurate localization problem based on DBNet. First, a convolutional attention mechanism is applied to the feature extraction network to get more valuable text feature maps. Then, a dual‐branch convolutional feature module is proposed in the feature pyramid to enrich contextual information. Besides, during the probability map generation stage, using a feature remodeling enhancement module to further distinguish text and text boundaries. This paper designs comparative experiments on ILTD, ICDAR2015 and MSRA‐TD500 datasets, achieve F‐measure of 92.3%, 86.0% and 84.1%, which are 2.2%, 2.3%, and 1.9% higher than DBNet, respectively. They demonstrate that our proposed method exhibits competitive performance and strong robustness. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

Enhancing scene text detectors with realistic text image synthesis using diffusion models

Scene text detection techniques have garnered significant attention due to their wide-ranging applications. However, existing methods have a high demand for training data, and obtaining accurate human annotations is labor-intensive and time-consuming. As a solution, researchers have widely adopted synthetic text images as a complementary resource to real text images during pre-training. Yet there is still room for synthetic datasets to enhance the performance of scene text detectors. We contend that one main limitation of existing generation methods is the insufficient integration of foreground text with the background. To alleviate this problem, we present the Diffusion Model based Text Generator (DiffText), a pipeline that utilizes the diffusion model to seamlessly blend foreground text regions with the background’s intrinsic features. Additionally, we propose two strategies to generate visually coherent text with fewer spelling errors. With fewer text instances, our produced text images consistently surpass other synthetic data in aiding text detectors. Extensive experiments on detecting horizontal, rotated, curved, and line-level texts demonstrate the effectiveness of DiffText in producing realistic text images. Code will be available at: https://github.com/99Franklin/DiffText.

Blockchain for video watermarking: An enhanced copyright protection approach for video forensics based on perceptual hash function.

As a direct result of advancements in digital technology and the Internet, the copyright protection and information integrity of multimedia that are being published across the Internet have emerged as a major and urgent issue that needs to be addressed. The technique of digital watermarking may be used to protect intellectual property. In terms of authentication, resilience, storage, and capacity of digital watermarking information, there is still room for development. Blockchain's potential in video copyright protection and management applications has motivated researchers. Copyright owners and consumers may now communicate directly via copyright protection apps built on the blockchain, eliminating the need for distributers and the associated fees. Nonetheless, the current blockchain-based video watermarking solutions require storing a significant number of coordinates depending on the watermark size and are susceptible to video frame attacks on the video frame texture region. This study proposes an enhanced video copyright management approach that incorporates digital watermarking, the blockchain, and a perceptual hash function. Watermark information is stored on a blockchain structure, which also acts as a timestamp for verification purposes. To verify watermark data without the source video, a perceptual hash function is employed to compute a hash value based on the structural information of video frames. The contribution is in learning how to extract a hash function from a small number of video frames that still adequately represent a large amount of video content while also reducing the number of unnecessary video frames and the amount of computation required to summarize and index that content in a blockchain. This expedites the dissemination of copyrighted works and increases their security and readability, hence facilitating their circulation on the Internet. Our experimental results demonstrate that this approach is memory efficient, as it only needs to store one key for each key frame, regardless of the size of the watermark. Additionally, the overall robustness is greatly improved by using the blockchain's random hash function. Therefore, new and important advancements in video watermarking have been realized because of this effort.

Understanding the grain refinement and residual stress formation mechanisms of a Ni-based alloy during machining processes

Ni-based alloys are important materials for many industries, microstructure of the sub-surface regions of these alloys can be refined during machining processes, affecting both their machinability and service performance. Several mechanisms have been proposed to explain the machining-induced microstructure refinement of these alloys, however, sound evidences to support them are still lacking. In this research, microstructure characterization with an emphasis on the micro-texture and defects was conducted on milled surfaces of a Ni-based alloy, to promote in-depth understanding of the grain-refinement process. A nano-crystalline layer consisted a topmost region with randomly-orientated grains and annealing nano-twins and a textured region beneath it with deformed microstructure was observed in the cross-section of the alloy with the help of high resolution orientation determination techniques. These observations provide additional evidences to support the recrystallization induced grain-refinement mechanism of the nano-grains. It is also suggested that recrystallization was a by-product of the shear deformation occurred during the milling process, supported by the observation of the clear boundary between the nano-grain layer and its beneath deformed region. The deformed region was refined as a result of mechanical twining and twin-intersections, leading to significant fluctuations of the residuals stresses.

The common element of test taking: Reading and responding to questions.

Taking a reading comprehension (RC) test is a goal-oriented task, with the goal of answering questions correctly. We assume the number of questions students correctly answer represents their ability to engage successfully in the RC processes necessary to understand texts. Students, however, use various test-taking strategies, some of which negatively impact passage comprehension. The present study used eye-tracking procedures to measure what students do when reading the one part of the tests that all students must read to perform well on an RC test, the questions. Participants included 248 third-, fifth-, and eighth-grade students who read six texts and responded to associated questions while researchers recorded their eye movements. Eye-movement records were used to code students' test-taking strategy and measure the time students spent reading multiple-choice questions and each response option. Students were also administered a measure of reading achievement. Analyses suggest eye movements on multiple-choice questions were associated with reading achievement, and the challenges less-skilled readers experience with texts are also present when reading in the question region. Differences in strategies and processes do not only occur in the text region. Therefore, researchers and practitioners should pay increased attention to the strategies that are taught and used by students when reading and responding to RC questions. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

A Comparative Model for Blurred Text Detection in Wild Scene Using Independent Component Analysis (ICA) and Enhanced Genetic Algorithm (Using a Bird Approach) with Classifiers

The advent of the study of Scene Text Detection and Recognition has exposed some significant challenges text recognition faces, such as blurred text detection. This study proposes a comparative model for detecting blurred text in wild scenes using independent component analysis (ICA) and enhanced genetic algorithm (E-GA) with support vector machine (SVM) and k-nearest neighbors (KNN) as classifiers. The proposed model aims to improve the accuracy of blurred text detection in challenging environments with complex backgrounds, noise, and illumination variations. The proposed model consists of three main stages: preprocessing, feature extraction, and classification. In the preprocessing stage, the input image is first preprocessed to remove noise and enhance edges using a median filter and a Sobel filter, respectively. Then, the blurred text regions are extracted using the Laplacian of Gaussian (LoG) filter. In the feature extraction stage, ICA is used to extract independent components from the blurred text regions. The extracted components are then fed into an E-GA-based feature selection algorithm to select the most discriminative features. The E-GA simply fine tunes the selection functionalities of the traditional GA using a bird approach. The selected features are then normalized and fed into the SVM and KNN classifiers. Experimental results on a benchmarking dataset (ICDAR 2019 LSVT) shows that the model outperforms state-of-the-art methods in terms of detection accuracy, precision, recall, and F1-score. The proposed model achieves an overall accuracy of 95.13% for SVM and 88.69% for KNN, which is significantly higher than the already existing methods which for SVM is 93%. In conclusion, the proposed model provides a promising approach for detecting blurred text in wild scenes. The combination of ICA, E-GA, and SVM/KNN classifiers enhances the robustness and accuracy of the detection system, which can be beneficial for a wide range of applications, such as text recognition, document analysis, and security systems.

A high-quality visual image encryption algorithm utilizing the conservative chaotic system and adaptive embedding

Regarding the issue of encrypted images in the channel attracting the attention of attackers and making them vulnerable to attacks. This paper proposes a high-quality visual image encryption algorithm utilizing the conservative chaotic system, two-dimensional compressive sensing, optimization local of binary patterns, and adaptive embedding method. Firstly, a conservative chaotic system with excellent performance and resistance to reconstruction attacks is proposed. Secondly, the pseudo-random sequences generated by the chaotic system dynamically generate measurement matrices, which are optimized before compressing the image. Then, during the encryption process, by utilizing the newly proposed composite DNA computing rules, chaotic sequences dynamically encode and compute image information, which enriches the coding criteria and improves the security of encryption algorithms. Finally, in the information embedding stage, the newly proposed OLBP algorithm can identify important textured and non-textured regions of the host image, prioritize embedding non-textured regions, and then embed textured regions, which can improve the amount of information embedding and reduce damage to the host image. Experimental simulation and analysis exhibit that the encryption algorithm has high security, strong robustness and high efficiency. Meanwhile the imperceptible analysis of the steganographic images is exceed 52 dB, so the algorithm presents a high-quality visual effect of imperceptibility.

HPNet: Text Detection Network with Hybrid Attention and Pixel Aggregation for Irregularly-Shaped Nearby Texts

Scene text detection is a challenging topic in computer vision, characterized by complex illumination, irregular shape, and arbitrary size. While recent advancements have been made in scene text detection, it remains difficult to simultaneously distinguish nearby text and accommodate irregularly shaped text. Therefore, this paper introduces HPNet, an enhanced text detector, based on the segmentation method that predicts two-scale results. To improve the shape robustness, the Hybrid Attentional Feature Fusion (HAFF) module is integrated into Feature Pyramid Networks (FPN) to dynamically perform feature fusion. Additionally, to distinguish nearby text, the model predicts the text region covering text instances and the text kernel covering the central region of the text. The improved Pixel Aggregation (PA) algorithm is then utilized to guide the expansion from the text kernel to the text region. Experiments on IC15, Total-Text, and CTW1500 validate the effectiveness of these improvements and the superiority of HPNet. Compared with the previous method PSENet for nearby texts, the proposed HPNet has improved inference speed by 63.6% and F-measure metric by 2.6%, 3.7%, and 2.5% on three datasets, respectively.

Turning a CLIP Model Into a Scene Text Spotter.

We exploit the potential of the large-scale Contrastive Language-Image Pretraining (CLIP) model to enhance scene text detection and spotting tasks, transforming it into a robust backbone, FastTCM-CR50. This backbone utilizes visual prompt learning and cross-attention in CLIP to extract image and text-based prior knowledge. Using predefined and learnable prompts, FastTCM-CR50 introduces an instance-language matching process to enhance the synergy between image and text embeddings, thereby refining text regions. Our Bimodal Similarity Matching (BSM) module facilitates dynamic language prompt generation, enabling offline computations and improving performance. FastTCM-CR50 offers several advantages: 1) It can enhance existing text detectors and spotters, improving performance by an average of 1.6% and 1.5%, respectively. 2) It outperforms the previous TCM-CR50 backbone, yielding an average improvement of 0.2% and 0.55% in text detection and spotting tasks, along with a 47.1% increase in inference speed. 3) It showcases robust few-shot training capabilities. Utilizing only 10% of the supervised data, FastTCM-CR50 improves performance by an average of 26.5% and 4.7% for text detection and spotting tasks, respectively. 4) It consistently enhances performance on out-of-distribution text detection and spotting datasets, particularly the NightTime-ArT subset from ICDAR2019-ArT and the DOTA dataset for oriented object detection.

Stereo Matching Method with Cost Volume Collaborative Filtering

Aiming at the problem of matching ambiguity and low disparity accuracy at the object boundary in stereo matching, a novel stereo matching algorithm with cost volume collaborative filtering is proposed. Firstly, for each pixel, two support windows are built, namely a local cross- support window as well as a global support window for the whole image. Secondly, a new adaptive weighted guide filter with a cross-support window as a kernel window is derived, and it is used to locally filter the cost volume. In addition, a minimum spanning tree is constructed in the whole image window, and then the minimum spanning tree filter is used to globally filter the cost volume. The collaborative filtering of cost volume is realized by fusing the filtering results of the local filter and global filter, so that each pixel can not only receive the support of the neighboring pixels in the local adaptive window, but can also receive the effective support of other pixels in the whole image, thus effectively eliminating the matching ambiguity in different texture regions while maintaining the disparity edges. The experimental results show that the average matching error rate of our method on the Middlebury stereo images is 3.17%. Compared with the other state-of-the-art methods, our method has higher robustness and matching accuracy, the generated disparity maps are smoother, and the disparity edges are better preserved.

PANNER: POS-Aware Nested Named Entity Recognition Through Heterogeneous Graph Neural Network

Nested named entity recognition (Nested NER) in knowledge graph (KG) aims at obtaining all meaningful entities, including nested entities for sentences in longer text region. Those obtained entities are to facilitate downstream applications, such as relation extraction, entity resolution, and coreference resolution. This task, however, is challenging not only because of the demand to detect the boundary of the entity but also due to the complexity of those hierarchically nested entities. Since a substantial amount of work has been made to Flat NER (or Nested NER), a few of them can explicitly acquire the position of the entity and utilize the grammatical construction of text. In this work, we propose PANNER, a POS-aware Nested NER model, to solve all the above issues. Specifically, we first construct a heterogeneous graph by introducing the part-of-speech (POS) information of the word. Second, we design a dilated random walk (DRW) algorithm based on a grammatical path to sample a fixed size of neighbors for each node. Third, we aggregate the message from different types of neighbors through an attention mechanism. Finally, we use a bidirectional decoding module to recognize and categorize all the flat and nested entities based on the node embedding in a layer-wise manner. Our extensive experiments show the effectiveness of PANNER in both flat and nested NER.

Spatial-frequency gradient fusion based model augmentation for high transferability adversarial attack

In recent years, deep learning has gained widespread application across diverse fields, including image classification and machine translation. Nevertheless, the emergence of adversarial examples has revealed a vulnerability of deep learning techniques to potential attacks. Despite the introduction of diverse adversarial attack methods, they are still constrained by certain limitations. Specifically, global perturbations are easily discernible by humans, resulting in poor imperceptibility. Additionally, current methods encounter limited transferability due to their reliance on attacking specific models. To address these challenges, this paper proposed a spatial-frequency gradient fusion based model augmentation for adversarial attack. First, we utilize a Gaussian convolution kernel to pinpoint regions in images that exhibit significant pixel variation, aiming to generate locally imperceptible perturbations undetectable by humans. These areas, which we consider as complex texture regions, are ideal for adding perturbations. Then, we design a perceptual similarity constraint to regulate the generation of perturbations in smooth texture regions. Subsequently, to further enhance the transferability of our method, we propose a spatial-frequency gradient fusion based model augmentation, applying random spectral transformation to shift into the frequency domain for narrowing the differences between models. Additionally, we design complex region scaling transformations in the spatial domain, aimed at capturing common features shared across models. Finally, we integrate the gradients from both the spatial and frequency domains, leveraging the strengths of both to empower attack models in effectively simulating the target model. Extensive experiments conducted on ImageNet and CIFAR-10 datasets have shown that our method attains a remarkable black-box attack success rate of up to 93.1%, with a perceptual loss reduction of approximately 8.39%, while also exhibiting stronger robustness.

Classification methods for hyperspectral remote sensing images with weak texture features

In order to provide method support for remote sensing image classification of weak texture region and improve the classification effect, a remote sensing image classification model for weak texture region was constructed. Firstly, the Gray Level Co-occurrence Matrix (GLCM) was used to extract texture features, then the correlation between texture features was reduced by dimensionality reduction method, and finally the spectral features were fused to classify remote sensing images. In the experimental part, neural network classifier was used to classify images based on texture feature, spectral feature and spectral features combined with texture features, and the results ware compared with object-oriented classification method. The analysis of texture extraction in the study area shows that the texture feature image obtained in the 5 × 5 window has higher resolution than that in the 7 × 7 window, and more robust texture features can be obtained in the range of 4∼8 pixel pairs. When using neural network classifier for classification, compared with images based on spectral features and texture features, the overall classification accuracy (OA) of the proposed classification model is increased by 16.72% and 11.16%, respectively, and the Kappa coefficient is increased by 0.2824 and 0.1943, respectively. Compared with the object-oriented classification method, the overall classification accuracy of the proposed classification scheme is increased by 1.15%, and the Kappa coefficient is increased by 0.0191. The constructed model has good classification effect and high classification accuracy for remote sensing images of weak texture region, and can provide scientific reference for remote sensing image classification of weak texture region.

Emergent chiral spin textures in centrosymmetric iron garnet with spin alignment constraints

Chiral spin textures have emerged as a captivating class of topological matter. These intriguing structures harbor localized and topologically protected magnetic textures, rendering them promising candidates for novel spintronic applications. Here, the emergent chiral spin textures in an iron garnet with nanodisk configuration have been micromagnatically simulated based on the Landau-Lifshitz-Gilbert equation (LLG) of COMSOL Multiphysics. The modification of magnetic spin texture in the considered iron garnet has been controlled by introducing the interfacial Dzyaloshinskii-Moriya interaction (I-DMI) and spin alignment constraints as locally pinned spins and curvilinear defects at edges. These boundary conditions induced interesting chiral textures not commonly observed in such iron garnet due to its centrosymmetric crystal structure. The simulation results showed the presence of two distinct regions of spin texture: inside and outside the pinning boundary. By systematically varying the DMI strength (D) with the size of the pinning boundary (Rpin) and the number of defects (Ndef), the emerging chiral spin textures have been explored and reported, including the existence of ramified helical stripes, skyrmions, and skyrmions-like textures such as elongated skyrmions, chiral horseshoe domains, biskyrmions and target skyrmions (TSk) (2π-TSk and 3π-TSk). Also, it has been reported that various collapses occur in the final spin texture states, leading to the transition from one state to another when varying the values of Rpin and Ndef with D.

Multi-gradient structures by cold extrusion of strongly textured continuously cast commercially pure aluminum

During extrusion, the material is subjected to a heterogeneous deformation that results in the formation of gradients of microstructure and thus mechanical properties. In this study, using continuously cast commercially pure aluminum as a reference material, we show how a cast structure with coarse columnar grains affects the formation of these gradients during extrusion at room temperature. Characterization of the initial material as well as the extruded round bars by optical and electron microscopy, X-ray diffraction as well as by means of mechanical testing documents the formation of four characteristic annular sections. For extrusion along the casting direction there is a <100>/<111> double-fiber textured center with lowest hardness, followed by a <111> single-fiber textured ring with a hardness plateau, a double-fiber textured region with grains arranged alternatingly in an iris-like shape, and an (ultra-)fine grained surface layer with highest hardness. For extrusion opposite to the casting direction we find similar characteristics of the center section. With increasing distance from the center follow another double-fiber textured section with increasing hardness, a <111> single-fiber textured ring with nearly homogeneous hardness and a surface layer with a slightly rotated <111> fiber and highest hardness. The key finding is that the macroscopic anisotropy of the cast material, resulting from the grain size, crystal orientation and growth direction of the columnar grains, determines the local material flow during extrusion and thus leads to the formation of these different complex multi-gradient macrostructures that may provide unique properties for complex applications such as light-weight safety-relevant components.

A Multi-Scale Natural Scene Text Detection Method Based on Attention Feature Extraction and Cascade Feature Fusion.

Scene text detection is an important research field in computer vision, playing a crucial role in various application scenarios. However, existing scene text detection methods often fail to achieve satisfactory results when faced with text instances of different sizes, shapes, and complex backgrounds. To address the challenge of detecting diverse texts in natural scenes, this paper proposes a multi-scale natural scene text detection method based on attention feature extraction and cascaded feature fusion. This method combines global and local attention through an improved attention feature fusion module (DSAF) to capture text features of different scales, enhancing the network's perception of text regions and improving its feature extraction capabilities. Simultaneously, an improved cascaded feature fusion module (PFFM) is used to fully integrate the extracted feature maps, expanding the receptive field of features and enriching the expressive ability of the feature maps. Finally, to address the cascaded feature maps, a lightweight subspace attention module (SAM) is introduced to partition the concatenated feature maps into several sub-space feature maps, facilitating spatial information interaction among features of different scales. In this paper, comparative experiments are conducted on the ICDAR2015, Total-Text, and MSRA-TD500 datasets, and comparisons are made with some existing scene text detection methods. The results show that the proposed method achieves good performance in terms of accuracy, recall, and F-score, thus verifying its effectiveness and practicality.

Deep Learning Techniques for Detecting and Segmenting Text in Natural Scene Images: Review

Text detection and segmentation in natural scene images is an active research problem in computer vision and document analysis. Unlike scanned documents, scene text exhibits significant diversity in appearance, orientation, scale, font, and lighting conditions. In this review, survey the current state-of-the-art in techniques and methodologies aimed at detecting and segmenting text regions from images of natural scenes are presented. Both traditional approaches using hand-crafted features as well as modern data-driven deep learning methods will be discussed. The review will analyze common datasets, evaluation protocols and metrics used for benchmarking. Limitations of existing methods and open challenges in handling multilingual text, curved text, and efficiency will be highlighted. Promising future directions towards robust and generalizable scene text extraction systems will be identified. In summary, the review will provide a comprehensive overview of the advances, remaining challenges and future opportunities in developing automated systems for detecting and segmenting text in unconstrained natural images.

Methodology for eliminating plain regions from captured images

Finding relevant content and extracting information from images is highly significant. Still, it may be challenging to do so because of changes within the textual contents, such as typefaces, size, line orientation, sophisticated backgrounds in images, and non-uniform illuminations. Despite these challenges, extracting content from captured images is still very important. Proficient textual content image recognition abilities extract text from the images to get over these issues. Despite the availability of several optical character recognition (OCR) techniques, this issue has yet to be resolved. Captured images with text are a rich source of information that should be presented so that viewers may make informed decisions. Because of this, it has become a complicated process to extract the text from an image because the text might be of poor quality, has a variety of fonts and styles, and occasionally have a complicated backdrop, among other things. Several approaches have been tried. However, finding a solution remains challenging. The maximally stable external regions (MSER) approach is developed to identify the text region in a picture. MSER is utilized to elevate the plain regions outside the text and non-text areas using geometric features and stroke width variation qualities.

Assessing readability of the text in ancient paper fragments by a photometric statistical analysis

Ancient documents are important historical sources that are often found in a fragmented condition due to their conservation status. In this study, we examined fragments of paper found in 1996 during excavation of theSanti Quattro Coronaticomplex, in Rome. The archaeological site where the fragments were found is situated on the first floor of the tower within the complex. This location was used as a disposal pit approximately between the 15th and 16th centuries. The fragments exhibit text discoloration, hindering automatic recognition and human readability. To reveal the faded text, the fragments have been digitalized, converted into a perceptually uniform color space and the contrast has been enhanced. The photometric characteristics of the input and enhanced images have been statistically characterized, and the contrast enhancement assessed by a state-of-the-art metric. The statistical analysis of the text colour coordinates was carried out to develop supervised and unsupervised image segmentation, isolating the text.The results of the method show that it effectively identifies text regions within images, improving readability, even for faded text. It can be integrated into deep learning-based character recognition systems, facilitating the automatic analysis of historical handwritten documents.