Text Images Research Articles

BCSR: toward arbitrarily oriented text image super-resolution via adaptive Bezier curve network

Although existing super-resolution networks based on deep learning have obtained good results, it is still challenging to achieve an ideal visual effect for irregular texts, especially spatially deformed ones. In this paper, we propose a robust Bezier Curve-based image super-resolution network (BCSR), which can efficiently handle the degradation caused by deformations. Firstly, the arbitrarily shaped text is adaptively fitted by a parameterized Bezier curve, aiming to convert a curved text box into an annotated text box. Then, we design a BezierAlign layer to calibrate between the extracted features and the input image. By importing the extracted text prior information, the accuracy of the super-resolution network can be significantly improved. It is worth highlighting that we propose a kind of text prior loss that enables the text prior image and the super-resolution text image to achieve cooperation enhancement. Extensive experiments on several standard scene text datasets demonstrate that our proposed model achieves desirable objective evaluation results and further immensely helps downstream tasks related to text recognition, especially in text instances with multi-orientation and curved shapes.

Text Image Super-Resolution Guided by Text Structure and Embedding Priors

We aim to super-resolve text images from unrecognizable low-resolution inputs. Existing super-resolution methods mainly learn a direct mapping from low-resolution to high-resolution images by exploring low-level features, which usually generate blurry outputs and suffer from severe structure distortion for text parts, especially when the resolution is quite low. Both the visual quality and the readability will suffer. To tackle these issues, we propose a new text super-resolution paradigm by recovering with understanding. Specifically, we extract a text-embedding prior and a text-structure prior from the upsampled image by learning to understand the text. The two priors with rich structure information and text-embedding information are then used as auxiliary information to recover the clear text structure. In addition, we introduce a text-feature loss to guide the training for better text recognizability. Extensive evaluations on both screen and scene text image datasets show that our method largely outperforms the state-of-the-art in both visual quality and recognition accuracy.

Синтезирующий этап в деятельности отраслевого переводчика: детерминация отрасли и технология формирования необходимых компетенций

In the article, using the activity approach developed in the works of A.N. Leontiev, translation is presented as a three-level succession of actions, including orientation and translation solution choice, its embodiment, coordinating the result obtained with the decision made. The synthesizing stage in the activity of an industry translator, within which the switching from the source language to the target language and the creation of the translated text is carried out, is considered in conjunction with the synthesizing stage of training in translation specialties in higher educational institutions. Existing in translation studies grounds enabled the author to show that the basis for translation text generation, including the selection of language means, is the image of the source text (its concept, meaning), which the translator must recreate in the translation text. Translation objectives that are relevant at this stage of professional activity are identified. The skills and abilities of a professional industry translator, which are necessary for performing the tasks, are characterized. It is substantiated that in order to form professional translation competence at the synthesizing stage of training, in addition to the disciplines provided for by the basic part of educational programs, it is necessary to introduce some new special disciplines into the training program.

TextStyleBrush: Transfer of Text Aesthetics From a Single Example.

We present a novel approach for disentangling the content of a text image from all aspects of its appearance. The appearance representation we derive can then be applied to new content, for one-shot transfer of the source style to new content. We learn this disentanglement in a self-supervised manner. Our method processes entire word boxes, without requiring segmentation of text from background, per-character processing, or making assumptions on string lengths. We show results in different text domains which were previously handled by specialized methods, e.g., scene text, handwritten text. To these ends, we make a number of technical contributions: (1) We disentangle the style and content of a textual image into a non-parametric, fixed-dimensional vector. (2) We propose a novel approach inspired by StyleGAN but conditioned over the example style at different resolution and content. (3) We present novel self-supervised training criteria which preserve both source style and target content using a pre-trained font classifier and text recognizer. Finally, (4) we also introduce Imgur5K, a new challenging dataset for handwritten word images. We offer numerous qualitative photo-realistic results of our method. We further show that our method surpasses previous work in quantitative tests on scene text and handwriting datasets, as well as in a user study.

SEMIOTIC ANALYSIS OF A LITERARY TEXT: SYMBOLIC DESCRIPTION

The article differentiates the theoretical side of the phenomenon of symbolic description related to semiotic science. Semiotics is the science of signs. In the semiotic analysis of a literary text, we encounter concepts such as symbol, marking, concept. Despite the fact that the listed concepts are superficially similar, it is important to determine the difference between them and to determine the subtext in the description of the author in the literary text. It also defines a symbolic description, a symbolic concept, a conceptual gap. Therefore, in our article we set out to highlight the differences between semiotics and its other structures, giving theoretical concepts concerning the transfer of a semiotic component in a literary text – a symbol, decoration and image of a literary text. In the article, we examined the main provisions in literary studies concerning the term symbol, considered the description of a symbolic image in a literary text as one of the semiotic analyses of a symbol. To identify a symbol from a literary text, we need to know its main features and its function in the text. Only then will we be able to determine the symbolic meaning of the symbol. Symbolic description not only increases the poetic power of a literary text, but also includes the code of sign systems. Therefore, the symbol can be considered both from the point of view of poetics and from the point of view of semiotics.

Retracted: Design and Construction of University Book Layout Based on Text Image Preprocessing Algorithm in Education Metaverse Environment.

[This retracts the article DOI: 10.1155/2022/6219401.].

EA-ConvNeXt: An Approach to Script Identification in Natural Scenes Based on Edge Flow and Coordinate Attention

In multilingual scene text understanding, script identification is an important prerequisite step for text image recognition. Due to the complex background of text images in natural scenes, severe noise, and common symbols or similar layouts in different language families, the problem of script identification has not been solved. This paper proposes a new script identification method based on ConvNext improvement, namely EA-ConvNext. Firstly, the method of generating an edge flow map from the original image is proposed, which increases the number of scripts and reduces background noise. Then, based on the feature information extracted by the convolutional neural network ConvNeXt, a coordinate attention module is proposed to enhance the description of spatial position feature information in the vertical direction. The public dataset SIW-13 has been expanded, and the Uyghur script image dataset has been added, named SIW-14. The improved method achieved identification rates of 97.3%, 93.5%, and 92.4% on public script identification datasets CVSI-2015, MLe2e, and SIW-13, respectively, and 92.0% on the expanded dataset SIW-14, verifying the superiority of this method.

Gradient-Based Graph Attention for Scene Text Image Super-resolution

Scene text image super-resolution (STISR) in the wild has been shown to be beneficial to support improved vision-based text recognition from low-resolution imagery. An intuitive way to enhance STISR performance is to explore the well-structured and repetitive layout characteristics of text and exploit these as prior knowledge to guide model convergence. In this paper, we propose a novel gradient-based graph attention method to embed patch-wise text layout contexts into image feature representations for high-resolution text image reconstruction in an implicit and elegant manner. We introduce a non-local group-wise attention module to extract text features which are then enhanced by a cascaded channel attention module and a novel gradient-based graph attention module in order to obtain more effective representations by exploring correlations of regional and local patch-wise text layout properties. Extensive experiments on the benchmark TextZoom dataset convincingly demonstrate that our method supports excellent text recognition and outperforms the current state-of-the-art in STISR. The source code is available at https://github.com/xyzhu1/TSAN.

Exploring Stroke-Level Modifications for Scene Text Editing

Scene text editing (STE) aims to replace text with the desired one while preserving background and styles of the original text. However, due to the complicated background textures and various text styles, existing methods fall short in generating clear and legible edited text images. In this study, we attribute the poor editing performance to two problems: 1) Implicit decoupling structure. Previous methods of editing the whole image have to learn different translation rules of background and text regions simultaneously. 2) Domain gap. Due to the lack of edited real scene text images, the network can only be well trained on synthetic pairs and performs poorly on real-world images. To handle the above problems, we propose a novel network by MOdifying Scene Text image at strokE Level (MOSTEL). Firstly, we generate stroke guidance maps to explicitly indicate regions to be edited. Different from the implicit one by directly modifying all the pixels at image level, such explicit instructions filter out the distractions from background and guide the network to focus on editing rules of text regions. Secondly, we propose a Semi-supervised Hybrid Learning to train the network with both labeled synthetic images and unpaired real scene text images. Thus, the STE model is adapted to real-world datasets distributions. Moreover, two new datasets (Tamper-Syn2k and Tamper-Scene) are proposed to fill the blank of public evaluation datasets. Extensive experiments demonstrate that our MOSTEL outperforms previous methods both qualitatively and quantitatively. Datasets and code will be available at https://github.com/qqqyd/MOSTEL.

Improving Scene Text Image Super-resolution via Dual Prior Modulation Network

Scene text image super-resolution (STISR) aims to simultaneously increase the resolution and legibility of the text images, and the resulting images will significantly affect the performance of downstream tasks. Although numerous progress has been made, existing approaches raise two crucial issues: (1) They neglect the global structure of the text, which bounds the semantic determinism of the scene text. (2) The priors, e.g., text prior or stroke prior, employed in existing works, are extracted from pre-trained text recognizers. That said, such priors suffer from the domain gap including low resolution and blurriness caused by poor imaging conditions, leading to incorrect guidance. Our work addresses these gaps and proposes a plug-and-play module dubbed Dual Prior Modulation Network (DPMN), which leverages dual image-level priors to bring performance gain over existing approaches. Specifically, two types of prior-guided refinement modules, each using the text mask or graphic recognition result of the low-quality SR image from the preceding layer, are designed to improve the structural clarity and semantic accuracy of the text, respectively. The following attention mechanism hence modulates two quality-enhanced images to attain a superior SR result. Extensive experiments validate that our method improves the image quality and boosts the performance of downstream tasks over five typical approaches on the benchmark. Substantial visualizations and ablation studies demonstrate the advantages of the proposed DPMN. Code is available at: https://github.com/jdfxzzy/DPMN.

COME: Clip-OCR and Master ObjEct for text image captioning

Text image captioning aims to understand the scene text in images for generating image captions. The key challenge of this task is to accurately and comprehensively understand the OCR tokens of scene text. Due to the dual modal of visual and textual features of scene text, expressing the multimodal semantic features of OCR tokens accurately is a challenging task. Additionally, since scene text cannot exist independently of specific objects and is always associated with its surroundings, establishing a scene graph centered around OCR tokens is also an important approach to understand its relationship with other objects in the image. In this paper, we propose a novel model named Clip-OCR and Master ObjEct (dubbed as COME) for text image captioning. First, we introduce a CLIP-OCR module to enhance the multimodal representation of OCR tokens. We separate the OCR representation into visual and textual items and narrow the similarity by contrastive learning. With the assistance of the CLIP-OCR module, we realize correlation alignment between different modes. Next, we propose the concept of master object for each OCR text and purify the OCR-oriented scene graph with it. The master object is defined as the object to which the OCR is attached, which bridges the semantic relationship between the OCR tokens and the image. We consider the master object as a proxy that connects OCR tokens and other regions in the image. By exploring the master object for each OCR token, we build a purified scene graph based on the master object and then enrich the visual embedding by the Graph Convolution Network (GCN). Furthermore, we cluster the OCR tokens and append the hierarchical information on the input embedding to provide a complete representation. Experiments on the TextCaps validation set and test set demonstrate the effectiveness of the proposed framework.

Improving scene text image captioning using transformer-based multilevel attention

Many existing image captioning methods only focus on image objects and their relationships for generating image captions, ignoring the text present in an image. Scene text (ST) contains crucial information to understand an image and facilitating reasoning. The existing methods fail to establish strong correlations between optical character recognition (OCR) tokens, as they have limited OCR representation power. Further, these methods have not efficiently used the positional information of the text. In this work, we have proposed an ST-based image captioning model (Trans-MAtt) based on a multilevel attention mechanism and relation network. We have used relation networks to enhance the connections between ST tokens. We have employed a multi-level attention method, which comprises of spatial, semantic, and appearance attention modules that precisely define the image. To represent context-enriched ST tokens, we use a combination of appearance, location, FastText, and PHOC features. We predict the ST location in the image, which is further integrated with the generated word embeddings for final caption generation. Experiments on the TextCaps dataset demonstrate the effectiveness of the proposed Trans-MAtt model, where it outperforms the current best model by 3.4% on B-4, 2.9% on METEOR, 3.3% on ROUGE-L, 3.1% on CIDEr-D, and 4.1% on SPICE metric scores. Our experiments on the Flickr30k and MSCOCO datasets demonstrated the superiority of our proposed model over existing methods.

Open set classification of untranscribed handwritten text image documents

Content-based classification of manuscripts is an important task that is generally carried out by expert archivists. Nevertheless, many historical manuscript collections are so vast that in most cases this task is hardly feasible, even for large, well staffed archives. Nowadays, manuscripts are generally preserved in the form of sets of digital images. Therefore, the technical problem we are interested in is automatic classification of “‘image documents”, each consisting of a set of untranscribed handwritten text images, by the textual contents of the images. The traditional Pattern Recognition classification paradigm does provide the basic tools to deal with this problem. However, in practice, the set of relevant classes of a large documental series is seldom known in advance. Therefore, a classifier trained with a predefined set of classes will systematically fail when new image documents arrive which do not belong to any of the classes assumed in training. Here we adopt the “Open Set Classification” framework to extend and consolidate our previous work on image document classification in order to adequately handle new documents from unknown classes. The proposed approaches are based on a relatively novel technology for text image representation known as “probabilistic indexing”, which proves very effective to characterise the intrinsic word-level uncertainty exhibited by historical handwritten text images. We assess the performance of this approach on a moderately sized but representative dataset extracted from a huge series of complex notarial manuscripts from the Spanish Archivo Histórico Provincial de Cádiz, with good results.

Perceiving Multiple Representations for scene text image super-resolution guided by text recognizer

Single image super-resolution (SISR) aims to recover clear high-resolution images from low-resolution images, which has made great progress with the development of deep learning these years. Scene text image super-resolution (STISR) is a subfield of SISR with the goal of increasing the resolution of a low-resolution text image and enhancing the readability of characters in the image. Despite significant improvements in recent approaches, STISR remains a challenging task due to the diversity of background, text appearances and layouts, etc. This paper presents a Perceiving Multiple Representations (PerMR) method for better super-resolution performances in scene text images. PerMR is a unified network that combines super-resolution with text recognition and exploits the recognizer’s feedback to facilitate super-resolution. Specifically, contextual information from the text decoder is extracted to provide sequence-specific guidance and enable the super-resolution model to pay more attention to the text region. Meanwhile, low-level and high-level visual features from the vision backbone of the recognition network are integrated to further improve visual quality. Additionally, we incorporate a frequency branch into the vanilla convolution unit, which efficiently enhances global and local feature representations. Experiments on the STISR benchmark dataset TextZoom validate that PerMR can not only generate more distinguishable images, but also outperforms the current state-of-the-art methods. PerMR boosts the average recognition accuracy by 5.9% using ASTER, 5.8% using MORAN and 10.6% using CRNN compared to the baseline model TSRN. PerMR outperforms the advanced method TPGSR-3 by 1.4% on ASTER, 0.1% on MORAN, 0.2% on CRNN and boosts TATT by 0.6% on ASTER and 1.1% on MORAN respectively. Furthermore, PerMR demonstrates good robustness and generalization when tackling low-quality text images in multiple scene text recognition datasets. The experiment results verify the capabilities of PerMR to boost text recognition performance.

Computational approaches for evaluating morphological changes in the corneal stroma associated with decellularization.

Decellularized corneas offer a promising and sustainable source of replacement grafts, mimicking native tissue and reducing the risk of immune rejection post-transplantation. Despite great success in achieving acellular scaffolds, little consensus exists regarding the quality of the decellularized extracellular matrix. Metrics used to evaluate extracellular matrix performance are study-specific, subjective, and semi-quantitative. Thus, this work focused on developing a computational method to examine the effectiveness of corneal decellularization. We combined conventional semi-quantitative histological assessments and automated scaffold evaluations based on textual image analyses to assess decellularization efficiency. Our study highlights that it is possible to develop contemporary machine learning (ML) models based on random forests and support vector machine algorithms, which can identify regions of interest in acellularized corneal stromal tissue with relatively high accuracy. These results provide a platform for developing machine learning biosensing systems for evaluating subtle morphological changes in decellularized scaffolds, which are crucial for assessing their functionality.

Deep image compression using scene text quality assessment

Image compression is a fundamental technology for Internet communication engineering. However, a high compression rate with general methods may degrade images, resulting in unreadable texts. In this paper, we propose an image compression method for maintaining text quality. We developed a scene text image quality assessment model to assess text quality in compressed images. The assessment model iteratively searches for the best-compressed image holding high-quality text. Objective and subjective results showed that the proposed method was superior to existing methods. Furthermore, the proposed assessment model outperformed other deep-learning regression models.

Classification of Arabic Alphabets Using a Combination of a Convolutional Neural Network and the Morphological Gradient Method

The field of Optical Character Recognition (OCR) is the process of converting an image of text into a machine-readable text format. The classification of Arabic manuscripts in general is part of this field. In recent years, the processing of Arabian image databases by deep learning architectures has experienced a remarkable development. However, this remains insufficient to satisfy the enormous wealth of Arabic manuscripts. In this research, a deep learning architecture is used to address the issue of classifying Arabic letters written by hand. The method based on a convolutional neural network (CNN) architecture as a self-extractor and classifier. Considering the nature of the dataset images (binary images), the contours of the alphabets are detected using the mathematical algorithm of the morphological gradient. After that, the images are passed to the CNN architecture. The available database of Arabic handwritten alphabets on Kaggle is utilized for examining the model. This database consists of 16,800 images divided into two datasets: 13,440 images for training and 3,360 for validation. As a result, the model gives a remarkable accuracy equal to 99.02%.

Deep architecture for super-resolution and deblurring of text images

Deep architecture for super-resolution and deblurring of text images

TLWSR: Weakly supervised real‐world scene text image super‐resolution using text label

AbstractScene text image super‐resolution (STISR) has recently received considerable attention. Existing STISR methods are applicable to the situation that all the LR‐HR pairs are available. However, in real‐world scenarios, it is difficult and expensive to collect ground‐truth HR labels and align them with LR images, and thus it is essential to find a way to implement weakly supervised learning. We investigate the STISR problem in the situation that only a subset of HR labels is available and design a weak supervision framework using coarse‐grained text labels named TLWSR, which combines incomplete supervision and inexact supervision. Specifically, a lightweight text recognition network and connectionist temporal classification loss are used to guide the super‐resolution of text images during training. Extensive experiments on the benchmark TextZoom demonstrate that TLWSR generates distinguishable text images and exceeds the fully supervised baseline TSRN in boosting text recognition accuracywith only 50% HR labels available. Meanwhile, TLWSR can be applied to different super‐resolution backbones and significantly improves their performance. Furthermore, TLWSR shows good generalization capability to low‐quality images on scene text recognition benchmarks, which verifies the effectiveness of this framework. To the authors' knowledge, this is the first work exploring the problem of STISR in weakly supervised scenarios.

Novel Heuristic Recurrent Neural Network Framework to Handle Automatic Telugu Text Categorization from Handwritten Text Image

In the near future, the digitization and processing of the current paper documents describe efficient role in the creation of a paperless environment. Deep learning techniques for handwritten recognition have been extensively studied by various researchers. Deep neural networks can be trained quickly thanks to a lot of data and other algorithmic advancements. Various methods for extracting text from handwritten manuscripts have been developed in literature. To extract features from written Telugu Text image having some other neural network approaches like convolution neural network (CNN), recurrent neural networks (RNN), long short-term memory (LSTM). Different deep learning related approaches are widely used to identification of handwritten Telugu Text; various techniques are used in literature for the identification of Telugu Text from documents. For automatic identification of Telugu written script efficiently to eliminate noise and other semantic features present in Telugu Text, in this paper, proposes Novel Heuristic Advanced Neural Network based Telugu Text Categorization Model (NHANNTCM) based on sequence-to-sequence feature extraction procedure. Proposed approach extracts the features using RNN and then represents Telugu Text in sequence-to-sequence format for the identification advanced neural network performs both encoding and decoding to identify and explore visual features from sequence of Telugu Text in input data. The classification accuracy rates for Telugu words, Telugu numerals, Telugu characters, Telugu sentences, and the corresponding Telugu sentences were 99.66%, 93.63%, 91.36%, 99.05%, and 97.73% consequently. Experimental evaluation describe extracted with revealed which are textured i.e. TENG shown considerable operations in applications such as private information protection, security defense, and personal handwriting signature identification.