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Given the ubiquity of handwriting and mathematical content in human transactions, machine recognition of handwritten mathematical text and symbols has become a domain of great practical scope and significance. Recognition of mathematical expression (ME) has remained a challenging and emerging research domain, with mathematical symbol recognition (MSR) as a requisite step in the entire recognition process. Many variations in writing styles and existing dissimilarities among the wide range of symbols and recurring characters make the recognition tasks strenuous even for Optical Character Recognition. The past decade has witnessed the emergence of recognition techniques and the peaking interest of several researchers in this evolving domain. In light of the current research status associated with recognizing handwritten math symbols, a systematic review of the literature seems timely. This article seeks to provide a complete systematic analysis of recognition techniques, models, datasets, sub-stages, accuracy metrics, and accuracy details in an extracted form as described in the literature. A systematic literature review conducted in this study includes pragmatic studies until the year 2021, and the analysis reveals Support Vector Machine (SVM) to be the most dominating recognition technique and symbol recognition rate to be most frequently deployed accuracy measure and other interesting results in terms of segmentation, feature extraction and datasets involved are vividly represented. The statistics of mathematical symbols-related papers are shown, and open problems are identified for more advanced research. Our study focused on the key points of earlier research, present work, and the future direction of MSR.

Handwritten character/symbol recognition is an important area of research in the present digital world. The solving of problems such as recognizing handwritten characters/symbols written in different styles can make the human job easier. Mathematical expression recognition using machines has become a subject of serious research. The main motivation for this work is both recognizing of the handwritten mathematical symbol, digits and characters which will be used for mathematical expression recognition. The system first identifies the contour in handwritten document segmentation and features extracted are given into SVM classifier for classification. GLCM and Zernike Moments are the two different feature extraction techniques used in this work. SVM with RBF kernel is used for classification. Zernike Moment features overperforms than GLCM. Zernike Moment achieves 97.89% accuracy and GLCM achieves 87.61% accuracy.

In the Fourth Industrial Revolution, artificial intelligence technology and big data science are emerging rapidly. To apply these informational technologies to the engineering industries, it is essential to digitize the data that are currently archived in image or hard-copy format. For previously created design drawings, the consistency between the design products is reduced in the digitization process, and the accuracy and reliability of estimates of the equipment and materials by the digitized drawings are remarkably low. In this paper, we propose a method and system of automatically recognizing and extracting design information from imaged piping and instrumentation diagram (P&ID) drawings and automatically generating digitized drawings based on the extracted data by using digital image processing techniques such as template matching and sliding window method. First, the symbols are recognized by template matching and extracted from the imaged P&ID drawing and registered automatically in the database. Then, lines and text are recognized and extracted from in the imaged P&ID drawing using the sliding window method and aspect ratio calculation, respectively. The extracted symbols for equipment and lines are associated with the attributes of the closest text and are stored in the database in neutral format. It is mapped with the predefined intelligent P&ID information and transformed to commercial P&ID tool formats with the associated information stored. As illustrated through the validation case studies, the intelligent digitized drawings generated by the above automatic conversion system, the consistency of the design product is maintained, and the problems experienced with the traditional and manual P&ID input method by engineering companies, such as time consumption, missing items, and misspellings, are solved through the final fine-tune validation process.

In ancient times, there was no system to record or document music. A basic notation system to write European music was formulated around 14th century in the Baroque period which slowly evolved into the standard notation system that we have today. Later, the musical pieces from the classical and post-classical period of European music were documented as scores using this standard European staff notations. These notations are used by most of the modern genres of music due to their versatility. Hence, it is very important to develop a method that can store such music sheets containing handwritten music scores digitally. Optical music recognition (OMR) is a system that automatically interprets the scanned handwritten music scores. In this work, we have proposed a classifier ensemble of deep transfer learning models with support vector machine (SVM) as the aggregator for handwritten music symbol recognition. We have applied three pre-trained deep learning models, namely ResNet50, GoogleNet and DenseNet161 (each trained on ImageNet), and fine-tuned on our target datasets i.e., music symbol image datasets. The proposed ensemble technique can capture a more complex association of the base classifiers, thus improving the overall performance. We have evaluated the proposed model on five publicly available standard datasets, namely Handwritten Online Music Symbols (HOMUS), Capitan_Score_Uniform, Capitan_Score_Non-uniform, Rebelo_real and Fornés, and achieved state-of-the-art results for all these datasets. Additionally, we have evaluated our model on publicly available two non-music symbols datasets, namely CMATERdb 2.1.2 containing 120 handwritten Bangla city names and CMATERdb 3.1.1 dataset containing handwritten Bangla numerals to validate its effectiveness on diversified datasets. The source code of this present work is available at https://github.com/ashis0013/Music-Symbol-Recognition .

This paper presents a review of deep learning on engineering drawings and diagrams. These are typically complex diagrams, that contain a large number of different shapes, such as text annotations, symbols, and connectivity information (largely lines). Digitising these diagrams essentially means the automatic recognition of all these shapes. Initial digitisation methods were based on traditional approaches, which proved to be challenging as these methods rely heavily on hand-crafted features and heuristics. In the past five years, however, there has been a significant increase in the number of deep learning-based methods proposed for engineering diagram digitalisation. We present a comprehensive and critical evaluation of existing literature that has used deep learning-based methods to automatically process and analyse engineering drawings. Key aspects of the digitisation process such as symbol recognition, text extraction, and connectivity information detection, are presented and thoroughly discussed. The review is presented in the context of a wide range of applications across different industry sectors, such as Oil and Gas, Architectural, Mechanical sectors, amongst others. The paper also outlines several key challenges, namely the lack of datasets, data annotation, evaluation and class imbalance. Finally, the latest development in digitalising engineering drawings are summarised, conclusions are drawn, and future interesting research directions to accelerate research and development in this area are outlined.

Point symbols on a scanned topographic map (STM) provide crucial geographic information. However, point symbol recognition entails high complexity and uncertainty owing to the stickiness of map elements and singularity of symbol structures. Therefore, extracting point symbols from STMs is challenging. Currently, point symbol recognition is performed primarily through pattern recognition methods that have low accuracy and efficiency. To address this problem, we investigated the potential of a deep learning-based method for point symbol recognition and proposed a deep convolutional neural network (DCNN)-based model for this task. We created point symbol datasets from different sources for training and prediction models. Within this framework, atrous spatial pyramid pooling (ASPP) was adopted to handle the recognition difficulty owing to the differences between point symbols and natural objects. To increase the positioning accuracy, the k-means++ clustering method was used to generate anchor boxes that were more suitable for our point symbol datasets. Additionally, to improve the generalization ability of the model, we designed two data augmentation methods to adapt to symbol recognition. Experiments demonstrated that the deep learning method considerably improved the recognition accuracy and efficiency compared with classical algorithms. The introduction of ASPP in the object detection algorithm resulted in higher mean average precision and intersection over union values, indicating a higher recognition accuracy. It is also demonstrated that data augmentation methods can alleviate the cross-domain problem and improve the rotation robustness. This study contributes to the development of algorithms and the evaluation of geographic elements extracted from STMs.

Background. Pahlavani and Zurkhaneh rituals have existed in Iran since the Parthian Empire. This martial art continued in the Islamic period and flourished due to cultural and social settings. A study on the evolution of this art specifies its prospering during the Qajar period as the Zurkhaneh became a common site in the urban context. Pahlavani martial arts play a significant role in depicting heroic behaviors and valiant tutoring and highlight unique architectural features and place emphasis on various cultural venues embedded within signs and symbols. Problem and Aim. The aim of this study is to portray what moral and cultural principles are applied to the structure and arrangement of the Zurkhanehs of the Qajar period. It also attempts to study the cultural and moral position of the Zurkhaneh within Iranian society. Recognition of the symbols and signs of this Pahlavani martial art can therefore acquaint us with the evolutionary process of the foundation of such institutions, and their cultural role, by analyzing concepts such as modesty, virtue and Futuwwa in the structure of the Zurkhanehs of Iran during the Qajar era. Methods. The present study investigates the issue under study via a descriptive-analytic methodology based on library resources and field observations. Conclusion. The findings of the research show that concepts of modesty, virtue, and Futuwwa are perceived in the Qajar Zurkhanehs of Tehran specifically in the way of entering and in the positioning of the Morshed and heroes.

This research introduces an artificial intelligence-based strategy for improving symbol recognition within the field of library science, concentrating on the creation and application of sophisticated technological solutions. Consistent with the objectives of the CHANGES project—Cultural Heritage Active Innovation for Sustainable Society, which focuses on the enhancement and management of cultural heritage through a multidisciplinary and interinstitutional approach—this strategy employs convolutional neural networks (CNNs) for accurate symbol classification. A CNN model was developed using an extensive dataset comprising over 6000 symbols, implementing meticulous preprocessing, feature extraction, and supervised learning protocols. The methodological pipeline incorporates advanced image segmentation techniques to isolate symbols from complex manuscripts, followed by data augmentation to enhance model resilience. The system is supported by a high-performance computing framework to manage large datasets efficiently, thereby facilitating more precise identification and analysis. This integration of machine learning techniques, exhaustive data management, and computational capabilities significantly advances existing symbol recognition methodologies, providing scholars with a potent tool for assisting in the classification and interpretation of historical symbols. The findings corroborate the potential of AI-enhanced symbol recognition in contributing to the broader objectives of computational library science and historical research.

On construction sites, it is crucial and and in most cases mandatory to wear safety equipment such as helmets, safety shoes, vests, and belts. The most important of these is the helmet, as it protects against head injuries and can also serve as a marker for detecting and tracking workers, since a helmet is typically visible to cameras on construction sites. Checking helmet usage, however, is a labor-intensive and time-consuming process. A lot of work has been conducted on detecting and tracking people. Some studies have involved hardware-based systems that require batteries and are often perceived as intrusive by workers, while others have focused on vision-based methods. The aim of this work is not only to detect workers and helmets, but also to identify workers through labeled helmets using symbol detection methods. Person and helmet detection tasks were handled by training existing datasets and gained accurate results. For symbol detection, 14 different shapes were selected and put on helmets in a triple format side by side. A total of 11,243 images have been annotated. YOLOv5 and YOLOv8 were used to train the dataset and obtain models. The results show that both methods achieved high precision and recall. However, YOLOv5 slightly outperformed YOLOv8 in real-time identification tests, correctly detecting the helmet symbols. A testing dataset containing different distances was generated in order to measure accuracy by distance. According to the results, accurate identification was achieved at distances of up to 10 meters. Also, a location-based symbol-ordering algorithm is proposed. Since symbol detection does not follow any order and works with confidence values in the inference mode, a left to right approach is followed.

From the very beginning of written scripts, contents of documents generally comprise of text, images, figures, graphs and graphic symbols. A graphic recognition system involves representation of graphic symbols, description of features extracted from the symbol and classification of the unknown symbols. Due to the wide range of symbols, no generalize technique is available that can recognize the symbol for all the application domains. this paper, we present an overview of the many models and methodologies available to symbol recognition for representation, description and classification. We provide a general survey of symbol recognition process, beginning with the basic definition of symbol, which is further classified into their types based on application areas. distinctive part of the survey is categorization of different symbol recognition methods into four categories i.e. statistical, structural, syntactical and hybrid methods, which is aimed to help potential researchers in exploring areas of research in the field of graphic symbol recognition.