Symbol Segmentation Research Articles

Scalable Hardware-Efficient Architecture for Frame Synchronization in High-Data-Rate Satellite Receivers

The continuous technical advancement of scientific space missions has resulted in a surge in the amount of data that is transferred to ground stations within short satellite visibility windows, which has consequently led to higher throughput requirements for the hardware involved. To aid synchronization algorithms, the communication standards commonly used in such applications define a physical layer frame structure that is composed of a preamble, segments of modulation symbols, and segments of pilot symbols. Therefore, the detection of a frame start becomes an essential operation, whose accuracy is undermined by the large Doppler shift and quantization errors in hardware implementations. In this work, we present a design methodology for frame synchronization modules that are robust against large frequency offsets and rely on a parallel architecture to support high throughput requirements. Several algorithms are evaluated in terms of the trade-off between accuracy and resource utilization, and the best solution is exemplified through its application to the CCSDS 131.2-B-1 and CCSDS 131.21-O-1 standards. The implementation results are reported for a Xilinx KU115 FPGA, thereby showing the capability of supporting baud rates that are greater than 2 Gbaud, as well as a corresponding throughput of 15.80 Gbps. To the best of our knowledge, this paper is the first to propose a design methodology for parallel frame synchronization modules that has applicability to the CCSDS 131.2-B-1 and CCSDS 131.21-O-1 standards.

Unleashing the power of old maps: Extracting symbology from nineteenth century maps using convolutional neural networks to quantify modern land use on historic wetlands

Topographical maps from the nineteenth century hold significant historical and environmental value, providing insights into landscape changes over the past two centuries. These maps feature distinct symbols representing various land cover types, such as forests and wetlands, offering a unique historical perspective on land-use changes. For example, there has been a significant reduction in wetlands because of agricultural expansion and intensification which lead to biodiversity loss and increased greenhouse gas emissions globally. Our study uses U-net CNN to automatically segment wetland symbols from nineteenth century maps from hundreds of map sheets for an area of interest a large river catchment in Ireland. Extracted wetland extents were intersected with digital land cover datasets to estimate current land cover on former wetland (on both organic and mineral soils). Utilizing U-Net, we successfully automated the segmentation of wetland symbols from hundreds of nineteenth-century map sheets, focusing on a large river catchment area in Ireland. Our analysis achieved a very high F1 score of 98.2% and a Kappa of 89%. While it is challenging to verify the veracity of historical map content, the largely untapped information contained within these maps are important for understanding landscape change over time, and especially before the era of Earth observation & remote sensing. The data extracted from these sources can inform modern environmental management strategies, for example, in targeted rewetting of peatlands, or in habitat restoration.

Offline handwritten mathematical expression recognition with graph encoder and transformer decoder

Handwritten mathematical expression recognition (HMER) has attracted extensive attention. Despite the significant progress achieved in recent years attributed to the development of deep learning approaches, HMER remains a challenge due to the complex spatial structure and variable writing styles. Encoder–decoder models with attention mechanism, which treats HMER as an image-to-sequence (i.e. LaTeX) generation task, have boosted the accuracy, but suffer from low interpretability in that the symbols are not segmented explicitly. Symbol segmentation is desired for facilitating post-processing and human interaction in real applications. In this paper, we formulate the mathematical expression as a graph and propose a Graph-Encoder-Transformer-Decoder (GETD) approach for HMER. For constructing the graph from input image, candidate symbols are first detected using an object detector and represented as the nodes of a graph, called symbol graph, and the edges of the graph encodes the between-symbol relationship. The spatial information is aggregated in a graph neural network (GNN), and a Transformer-based decoder is used to identify the symbol classes and structure from the graph. Experiments on public datasets demonstrate that our GETD model achieves competitive expression recognition performance while offering good interpretability compared with previous methods.

SUBJECT MATTER OF SECURITY SOCIOLOGY INSTANCE OF CRITICAL SOCIOLOGY

In the essay Im looking for a subject matter of sociological research concepts formation strategies in sociological works of Vida Bajc from The University of Yale. Ms. Bajc analysis of modern security apparatus follows such paths of critical sociology of security which takes especially bureaucratic methods of absolute security production into account. Special research subjects of Ms. Bajc are techniques of public spaces creation and control. Cognition of secu-rity according to Vida Bajc is anchored in patterns of European culture indi-vidualism. In such frames of reference asserts itself real and symbolic segmen-tation of society in contradistinction to oriental holistic culture and its ac-ceptance of ritual insecurity. In my commentary of critical sociology of Ms. Bajc Im searching for a web of sociological concepts which delineate sociol-ogy of security research subject.

Four ecotourism archetypes: expressing symbolic desires

ABSTRACT Most ecotourism research focuses on logical, expressed desires, while deeper meanings (or symbolic desires), are less well understood, perhaps because elicitation is difficult. Consequently, prior research is relatively limited on the deeper motivations driving ecotourism behaviour. To address that lack, an ethnographic study of nature-based tourism activities in Malaysia and a symbolic interactionist approach is taken to unpack hedonic meaning-making. The data gave rise to four ecotourist archetypes: The champion, the adherent, the acolyte, and the spa-goer, for ecotourists seeking trial, spiritual, learning and purification benefits, respectively. The study provides both greater insight into how to use ethnographic research to elicit symbolic desires in ecotourism, and practitioner support for the design and delivery of more fulfilling nature-based tourism experiences. The archetypes can be viewed as a symbolic benefits-based market segmentation schema, forming the basis for developing more effective targeting, positioning, branding and value propositions for nature-based tourism activities.

Instance GNN: A Learning Framework for Joint Symbol Segmentation and Recognition in Online Handwritten Diagrams

Online handwritten diagram recognition (OHDR) has attracted considerable attention for its potential applications in many areas, but it is a challenging task due to the complex 2D structure, writing style variation, and lack of annotated data. Existing OHDR methods often have limitations in modeling and learning complex contextual relationships. To overcome these challenges, we propose an OHDR method based on graph neural networks (GNNs) in this paper. In particular, we formulate symbol segmentation and symbol recognition as node clustering and node classification problems on stroke graphs and solve the problems jointly under a unified learning framework with a GNN model. This GNN model is denoted as Instance GNN since it gives the symbol instance label as well as the semantic label. Extensive experiments on two flowchart datasets and a finite automata dataset show that our method consistently outperforms previous methods with large margins and achieves state-of-the-art performance. In addition, we release a large-scale annotated online handwritten flowchart dataset, CASIA-OHFC, and provide initial experimental results as a baseline.

A mixed image segmentation method based on intelligent equipment

Taking Uyghur character recognition as an example, this paper use a method of image segmentation which combines traditional methods with CNN, and transforms and implements the Uyghur character on intelligent devices. Firstly, after analyzing the application of several general segmentation methods, this paper finds some shortcomings but also some ideas for Uighur segmentation. Then, starting from the characteristics of Uyghur language, such as structure, word formation and input habits, the author studies the idea of Uyghur adhesive language segmentation from the perspective of language characteristics, puts forward the basic algorithm of Uyghur symbol segmentation, and applies the Uyghur character adhesion segmentation based on minimum spanning tree and multi-queue primitive merging model to improve the segmentation efficiency. In addition, in order to solve the limitations of the traditional handwriting recognition framework of “preprocessing + feature extraction + classifier”, this paper puts forward a new solution of Uyghur handwriting recognition technology combining prior domain knowledge with CNN, constructs a Uyghur handwriting recognition model on the basis of CNN and random elastic deformation, and also improves the recognition rate of the system by combining domain knowledge with CNN. According to the experimental results, we can conclude that the mixed method proposed in this paper can effectively break the technical bottleneck of traditional methods, thus improving the efficiency of segmentation and recognition.

Stroke constrained attention network for online handwritten mathematical expression recognition

In this paper, we propose a novel stroke constrained attention network (SCAN) which treats stroke as the basic unit for encoder-decoder based online handwritten mathematical expression recognition (HMER). Unlike previous methods which use trace points or image pixels as basic units, SCAN makes full use of stroke-level information for better alignment and representation. The proposed SCAN can be adopted in both single-modal (online or offline) and multi-modal HMER. For single-modal HMER, SCAN first employs a CNN-GRU encoder to extract point-level features from input traces in online mode and employs a CNN encoder to extract pixel-level features from input images in offline mode, then use stroke constrained information to convert them into online and offline stroke-level features. Using stroke-level features can explicitly group points or pixels belonging to the same stroke, therefore reduces the difficulty of symbol segmentation and recognition via the decoder with attention mechanism. For multi-modal HMER, other than fusing multi-modal information in decoder, SCAN can also fuse multi-modal information in encoder by utilizing the stroke based alignments between online and offline modalities. The encoder fusion is a better way for combining multi-modal information as it implements the information interaction one step before the decoder fusion so that the advantages of multiple modalities can be exploited earlier and more adequately. Besides, we propose an approach combining the encoder fusion and decoder fusion, namely encoder-decoder fusion, which can further improve the performance. Evaluated on a benchmark published by CROHME competition, the proposed SCAN achieves the state-of-the-art performance. Furthermore, by conducting experiments on an additional task: online handwritten Chinese character recognition (HCCR), we demonstrate the generality of our proposed method.

Graph-to-Graph: Towards Accurate and Interpretable Online Handwritten Mathematical Expression Recognition

Recent handwritten mathematical expression recognition (HMER) approaches treat the problem as an image-to-markup generation task where the handwritten formula is translated into a sequence (e.g. LaTeX). The encoder-decoder framework is widely used to solve this image-to-sequence problem. However, (i) for structured mathematical formula, the hierarchical structure neither in the formula nor in the markup has been explored adequately. In addition, (ii) existing image-to-markup methods could not explicitly segment mathematical symbols in the formula corresponding to each target markup token. In this paper, we address the above issues by formulating the HMER as a graph-to-graph (G2G) learning problem. Graph is more flexible and general for structure representation and learning compared with image or sequence. At the core of our method lies the embedding of input formula and output markup into graphs on primitives, with Graph Neural Networks (GNN) to explore the structural information, and a novel sub-graph attention mechanism to match primitives in the input and output graphs. We conduct extensive experiments on CROHME datasets to demonstrate the benefits of the proposed G2G model. Our method yields significant improvements over previous SOTA image-to-markup systems. Moreover, it explicitly resolves the symbol segmentation problem while still being trained end-to-end, making the whole system much more accurate and interpretable.

SKETRACK: Stroke-Based Recognition of Online Hand-Drawn Sketches of Arrow-Connected Diagrams and Digital Logic Circuit Diagrams

Digitalization of handwritten documents has created a greater need for accurate online recognition of hand-drawn sketches. However, the online recognition of hand-drawn diagrams is an enduring challenge in human-computer interaction due to the complexity in extracting and recognizing the visual objects reliably from a continuous stroke stream. This paper focuses on the design and development of a new, efficient stroke-based online hand-drawn sketch recognition scheme named SKETRACK for hand-drawn arrow diagrams and digital logic circuit diagrams. The fundamental parts of this model are text separation, symbol segmentation, feature extraction, classification, and structural analysis. The proposed scheme utilizes the concepts of normalization and segmentation to isolate the text from the sketches. Then, the features are extracted to model different structural variations of the strokes that are categorized into the arrows/lines and the symbols for effective processing. The strokes are clustered using the spectral clustering algorithm based on p-distance and Euclidean distance to compute the similarity between the features and minimize the feature dimensionality by grouping similar features. Then, the symbol recognition is performed using modified support vector machine (MSVM) classifier in which a hybrid kernel function with a lion optimized tuning parameter of SVM is utilized. Structural analysis is performed with lion-based task optimization for recognizing the symbol candidates to form the final diagram representations. This proposed recognition model is suitable for simpler structures such as flowcharts, finite automata, and the logic circuit diagrams. Through the experiments, the performance of the proposed SKETRACK scheme is evaluated on three domains of databases and the results are compared with the state-of-the-art methods to validate its superior efficiency.

Track, Attend, and Parse (TAP): An End-to-End Framework for Online Handwritten Mathematical Expression Recognition

In this paper, we introduce Track, Attend, and Parse (TAP), an end-to-end approach based on neural networks for online handwritten mathematical expression recognition (OHMER). The architecture of TAP consists of a tracker and a parser. The tracker employs a stack of bidirectional recurrent neural networks with gated recurrent units (GRU) to model the input handwritten traces, which can fully utilize the dynamic trajectory information in OHMER. Followed by the tracker, the parser adopts a GRU equipped with guided hybrid attention (GHA) to generate notations. The proposed GHA is composed of a coverage-based spatial attention, a temporal attention, and an attention guider. Moreover, we demonstrate the strong complementarity between offline information with static-image input and online information with ink-trajectory input by blending a fully convolutional networks-based watcher into TAP. Inherently, unlike traditional methods, this end-to-end framework does not require the explicit symbol segmentation and a predefined expression grammar for parsing. Validated on a benchmark published by the CROHME competition, the proposed approach outperforms the state-of-the-art methods and achieves the best reported results with an expression recognition accuracy of 61.16% on CROHME 2014 and 57.02% on CROHME 2016, using only official training dataset.

AUTOMATION OF VEHICLE PLATE NUMBERS IDENTIFICATION ON ONE-ASPECT IMAGES

The subject matter of the article is the method of automating the identification of vehicle plate numbers based on the processing of one-aspect images obtained using video recording means. The goal is to provide automation of the process of identifying vehicle plate numbers within a wide range of changing the viewing angles and the levels of illumination. The task is formulation of the method of automated identification of vehicle plate numbers on one-aspect images, which are obtained by means of video fixation within wide limits of changing both the viewing angles and the levels of illumination. Analysis of the problems of methods and algorithms of automated detection and recognition of vehicle plate numbers has shown that it is most promising to use flexible algorithms that adapt to the changing conditions of observation of traffic control devices. One of the promising technologies for implementing such algorithms is the application of artificial neural networks. The solution of the problem of recognition of vehicle plate numbers can be represented as a complex of image processing and analysis of algorithms, which includes the initial preparation of the image, the discovery of the area of the vehicle plate on the image, the segmentation of symbols and the recognition of symbols. Conclusions: an algorithmically implemented method of identifying vehicle plate numbers, which makes possible searching the text areas under an arbitrary angle in different lighting conditions, is proposed. This method allows automating the process of identification of vehicle plate numbers within a wide range of distances to the car, as well as viewing the angles and levels of illumination. The purpose of further research is to improve the proposed method for its implementation, using modern software and hardware.

A Covariance-matching-based Model for Musical Symbol Recognition

A musical sheet is read by optical music recognition (OMR) systems that automatically recognize and reconstruct the read data to convert them into a machine-readable format such as XML so that the music can be played. This process, however, is very challenging due to the large variety of musical styles, symbol notation, and other distortions. In this paper, we present a model for the recognition of musical symbols through the use of a mobile application, whereby a camera is used to capture the input image; therefore, additional difficulties arise due to variations of the illumination and distortions. For our proposed model, we first generate a line adjacency graph (LAG) to remove the staff lines and to perform primitive detection. After symbol segmentation using the primitive information, we use a covariance-matching method to estimate the similarity between every symbol and pre-defined templates. This method generates the three hypotheses with the highest scores for likelihood measurement. We also add a global consistency (time measurements) to verify the three hypotheses in accordance with the structure of the musical sheets; one of the three hypotheses is chosen through a final decision. The results of the experiment show that our proposed method leads to promising results.

Robust vectorization method for electrical circuit drawings using component morphology

Decomposition and representation of electrical circuit drawings to a suitable vector form has widespread applications related to data compression, storage, analysis, and editing. In this paper, we propose an efficient method for segmenting and identifying electrical circuit symbols from the image of a circuit drawing with an objective of its complete vectorization. The segmentation procedure is based on morphological operations with automatic selection of structuring elements. After segmentation, the symbols are identified by an SVM classifier that works with certain morphological and topological features of the segmented symbols. Related information like orientations and terminal attributes of the circuit symbols are finally stored in appropriate vector form. Text annotations associated with the symbols are also recognized and vectorized. Detailed performance analysis demonstrates the efficiency and robustness of the proposed method.

Неустойчивый баланс между коллективным и индивидуальным началами в культуре. Свобода как индикатор равновесия

<p>Статья посвящена рассмотрению взаимодействия и баланса коллективной и индивидуальной форм жизнедеятельности и соответствующих культур, исторических тенденций этого процесса и его связанности со становлением и развитием представлений о свободе, с доминантными формами идеологии. </p><p>Обосновывается единство целей социального и символического сегментов культуры по обеспечению социальной интеграции, как непосредственного, так и опосредованного. Изначально культура была сосредоточена на апологии коллективного начала в жизнедеятельности. «Реабилитация» индивидуального начала в культуре начинается с искусства итальянского Ренессанса. В социальной жизни процесс индивидуализации начался с возникновения дуэли как акции по защите личного достоинства и системы идентификации человека по имени и фамилии. Современная эпоха отличается высоким уровнем развития индивидуальной культуры и сокращением сферы коллективной культуры.</p><p>Но речь не идет о вытеснении одного другим, а только о балансе обоих начал. Этот баланс выражается в масштабе свободы. Свобода и есть выражение актуального психологически комфортного баланса коллективного и индивидуального начал в культуре. Расширение свободы — это усиление индивидуального начала. Наглядней всего этот баланс выражен в господствующей идеологии. Этот баланс исторически неустойчив и связан с преобладанием того или иного социально-политического сегмента в обществе. Но в любом случае общество нуждается в поддержании психологически комфортного соотношения между коллективной и индивидуальной культурами.</p>

Watch, attend and parse: An end-to-end neural network based approach to handwritten mathematical expression recognition

Machine recognition of a handwritten mathematical expression (HME) is challenging due to the ambiguities of handwritten symbols and the two-dimensional structure of mathematical expressions. Inspired by recent work in deep learning, we present Watch, Attend and Parse (WAP), a novel end-to-end approach based on neural network that learns to recognize HMEs in a two-dimensional layout and outputs them as one-dimensional character sequences in LaTeX format. Inherently unlike traditional methods, our proposed model avoids problems that stem from symbol segmentation, and it does not require a predefined expression grammar. Meanwhile, the problems of symbol recognition and structural analysis are handled, respectively, using a watcher and a parser. We employ a convolutional neural network encoder that takes HME images as input as the watcher and employ a recurrent neural network decoder equipped with an attention mechanism as the parser to generate LaTeX sequences. Moreover, the correspondence between the input expressions and the output LaTeX sequences is learned automatically by the attention mechanism. We validate the proposed approach on a benchmark published by the CROHME international competition. Using the official training dataset, WAP significantly outperformed the state-of-the-art method with an expression recognition accuracy of 46.55% on CROHME 2014 and 44.55% on CROHME 2016.

A system for recognizing online handwritten mathematical expressions by using improved structural analysis

A system for recognizing online handwritten mathematical expressions (MEs), by applying improved structural analysis, is proposed and experimentally evaluated on two databases. With this system, MEs are represented in the form of stochastic context-free grammar (SCFG), and the Cocke---Younger---Kasami (CYK) algorithm is used to parse two-dimensional (2D) structures of online handwritten MEs and select the best interpretation in terms of the results of symbol segmentation and recognition as well as structural analysis. A concept of "body box" is proposed, and two SVM models are applied for learning and analyzing structural relations from training patterns without the need for any heuristic decisions. Stroke order is used to reduce the complexity of the parsing algorithm. Even though SCFG does not resolve ambiguities in some cases, the proposed system still gives users a list of candidates that contains the expected result. The results of experimental evaluations of the proposed system on the CROHME 2013 and CROHME 2014 databases and on an in-house ("Hand-Math") database show that the recognition rate of the proposed system is improved, while the processing time on a common CPU is kept to a practical level.

Online recognition of sketched arrow-connected diagrams

We introduce a new, online, stroke-based recognition system for hand-drawn diagrams which belong to a group of documents with an explicit structure obvious to humans but only loosely defined from the machine point of view. We propose a model for recognition by selection of symbol candidates, based on evaluation of relations between candidates using a set of predicates. It is suitable for simpler structures where the relations are explicitly given by symbols, arrows in the case of diagrams. Knowledge of a specific diagram domain is used--the two domains are flowcharts and finite automata. Although the individual pipeline steps are tailored for these, the system can readily be adapted for other domains. Our entire diagram recognition pipeline is outlined. Its core parts are text/non-text separation, symbol segmentation, their classification and structural analysis. Individual parts have been published by the authors previously and so are described briefly and referenced. Thorough evaluation on benchmark databases shows the accuracy of the system reaches the state of the art and is ready for practical use. The paper brings several contributions: (a) the entire system and its state-of-the-art performance; (b) the methodology exploring document structure when it is loosely defined; (c) the thorough experimental evaluation; (d) the new annotated database for online sketched flowcharts and finite automata diagrams.

Advancing the state of the art for handwritten math recognition: the CROHME competitions, 2011–2014

The CROHME competitions have helped organize the field of handwritten mathematical expression recognition. This paper presents the evolution of the competition over its first 4 years, and its contributions to handwritten math recognition, and more generally structural pattern recognition research. The competition protocol, evaluation metrics and datasets are presented in detail. Participating systems are analyzed and compared in terms of the central mathematical expression recognition tasks: (1) symbol segmentation, (2) classification of individual symbols, (3) symbol relationships and (4) structural analysis (parsing). The competition led to the development of label graphs, which allow recognition results with conflicting segmentations to be directly compared and quantified using Hamming distances. We introduce structure confusion histograms that provide frequencies for incorrect subgraphs corresponding to ground-truth label subgraphs of a given size and present structure confusion histograms for symbol bigrams (two symbols with a relationship) for CROHME 2014 systems. We provide a novel analysis combining results from competing systems at the level of individual strokes and stroke pairs; this virtual merging of system outputs allows us to more closely examine limitations for current state-of-the-art systems. Datasets along with evaluation and visualization tools produced for the competition are publicly available.

EVALUATION OF COMPONENT ALGORITHMS IN AN ALGORITHM SELECTION APPROACH FOR SEMANTIC SEGMENTATION BASED ON HIGH-LEVEL INFORMATION FEEDBACK

In this paper we discuss certain theoretical properties of the algorithm selection approach to the problem of semantic segmentation in computer vision. High quality algorithm selection is possible only if each algorithm’s suitability is well known because only then the algorithm selection result can improve the best possible result given by a single algorithm. We show that an algorithm’s evaluation score depends on final task; i.e. to properly evaluate an algorithm and to determine its suitability, only well formulated tasks must be used. When algorithm suitability is well known, the algorithm can be efficiently used for a task by applying it in the most favorable environmental conditions determined during the evaluation. The task dependent evaluation is demonstrated on segmentation and object recognition. Additionally, we also discuss the importance of high level symbolic knowledge in the selection process. The importance of this symbolic hypothesis is demonstrated on a set of learning experiments with a Bayesian Network, a SVM and with statistics obtained during algorithm selector training. We show that task dependent evaluation is required to allow efficient algorithm selection. We show that using symbolic preferences of algorithms, the accuracy of algorithm selection can be improved by 10 to 15% and the symbolic segmentation quality can be improved by up to 5% when compared with the best available algorithm.