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Journal articles on the topic 'Recognize text'

Author: Grafiati

Published: 28 May 2022

Last updated: 31 July 2025

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1

Li, Wenzhe, and Tracy Hammond. "Recognizing Text Through Sound Alone." Proceedings of the AAAI Conference on Artificial Intelligence 25, no. 1 (2011): 1481–86. http://dx.doi.org/10.1609/aaai.v25i1.7987.

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This paper presents an acoustic sound recognizer to recognize what people are writing on a table or wall by utilizing the sound signal information generated from a key, pen, or fingernail moving along a textured surface. Sketching provides a natural modality to interact with text, and sound is an effective modality for distinguishing text. However, limited research has been conducted in this area. Our system uses a dynamic time- warping approach to recognize 26 hand-sketched characters (A-Z) solely through their acoustic signal. Our initial prototype system is user-dependent and relies on fixe

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Aanchal, Garg, Chauhan Robin, and Kumar Tomar Sanjiv. "DETECT AND RECOGNIZE APP." Journal of Advanced and Applied Sciences 1, no. 2 (2023): 1–10. https://doi.org/10.5281/zenodo.8007346.

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The aim of this project was to develop an app that is able to recognize the text, object, landmark, face and banknote using the concept of machine learning and various approaches to detect text, faces, banknote, landmark, etc. This project is designed mainly for blind people. As a result, we learned about the machine learning, uses of machine learning, TensorFlow, ways to implement TensorFlow in our android app, firebase, ways to implement firebase, convolutional neural networks, how to train a model using a dataset, how to implement a trained model in an Android app, how to design an Android

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Agrawal, Atul, and Omprakesh Singh. "Large Scale Short Text Analysis to Recognize Categories." International Journal of Computer Sciences and Engineering 7, no. 5 (2019): 1873–77. http://dx.doi.org/10.26438/ijcse/v7i5.18731877.

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Khudhair, Khamael A., and Itimad Raheem Ali. "Using Feature Extraction to Recognize Handwritten Text Image." International Journal of Scientific & Engineering Research 5, no. 1 (2014): 89–95. http://dx.doi.org/10.14299/ijser.2014.01.003.

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Zhou, Zhao, Xiangcheng Du, Yingbin Zheng, Xingjiao Wu, and Cheng Jin. "An Exemplar-based Framework for Chinese Text Recognition." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 10 (2025): 10896–904. https://doi.org/10.1609/aaai.v39i10.33184.

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This paper introduces a novel exemplar-based framework for reading Chinese texts in natural scene or document images. We present the Deep Exemplar-based Chinese Text Recognizer, which is structured to first identify candidate characters as exemplars from each text-line, and subsequently recognize them by retrieving analogous exemplars from a database. With text-line level annotations, we design the exemplar discovery network to simultaneously recognize texts and capture individual character positions in a weak-supervision manner. The exemplar retrieval module is then crafted to identify the mo

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Mr., B. Ravinder Reddy, Nandini J., and Sowmya |. Y. Sathwik P. "Handwritten Text Recognition and Digital Text Conversion." International Journal of Trend in Scientific Research and Development 3, no. 3 (2019): 1826–27. https://doi.org/10.31142/ijtsrd23508.

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Sometimes it is extremely difficult to secure handwritten documents in the real world. While doing so, we may encounter many problems such as misplacing the documents, unavailability of access from anywhere, physical damage, etc. So, to keep the information secure, we convert that information into digital format to address all the above mentioned problems. The main aim of our application is to recognize hand written text and display it in digital text format. Image processing is very significant process for data analysis these days. In image processing, the visible text from the real world as

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Vinokurov, Igor Victorovich. "Using a Convolutional Neural Network to Recognize Text Elements in Poor Quality Scanned Images." Program Systems: Theory and Applications 13, no. 3 (2022): 45–59. http://dx.doi.org/10.25209/2079-3316-2022-13-3-45-59.

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The paper proposes a method for recognizing the content of scanned images of poor quality using convolutional neural networks (CNNs). The method involves the implementation of three main stages. At the first stage, image preprocessing is implemented, which consists of identifying the contours of its alphabetic and numeric elements and basic punctuation marks. At the second stage, the content of the image fragments inside the identified contours is sequentially fed to the input of the CNN, which implements a multiclass classification. At the third and final stage, the post-processing of the set

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Yengantiwar, Prof Tarun. "Image Text Detection and Conversion into Text Form." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem32236.

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We are purposing a system by which we can detect the image text and convert into the text form for which we firstly apply MSER (Maximally Stable External Region) which is used as a method of blob detection in images, or to detect character candidates. After that some text features by which the text can be recognized. To recognize the text feature we apply some geometric filtration by which we can easily identified the character from the image. This is why all the character is formed by combination of geometric figures. After recognizing the text, reject the false positives i.e. background, fig

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Umair, Muhammad, Muhammad Zubair, Farhan Dawood, et al. "A Multi-Layer Holistic Approach for Cursive Text Recognition." Applied Sciences 12, no. 24 (2022): 12652. http://dx.doi.org/10.3390/app122412652.

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Urdu is a widely spoken and narrated language in several South-Asian countries and communities worldwide. It is relatively hard to recognize Urdu text compared to other languages due to its cursive writing style. The Urdu text script belongs to a non-Latin cursive family script like Arabic, Hindi and Chinese. Urdu is written in several writing styles, among which ‘Nastaleeq’ is the most popular and widely used font style. A gap still poses a challenge for localization/detection and recognition of Urdu Nastaleeq text as it follows modified version of Arabic script. This research study presents

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Dewi Budiwati, Sari, Dahliar Ananda, and Siska Komala Sari. "Review on Text Extraction in Complex Image using Five Different Web OCR." International Journal of Engineering & Technology 8, no. 1.9 (2019): 199–204. http://dx.doi.org/10.14419/ijet.v8i1.9.26399.

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Price product comparison is needed for the consumer in order to find the cheapest price. In this research, we build the system to compare the price between several modern markets. The input is product catalogs since consumer often receives it from the modern market. We upload 75 grayscale 75 color input images into five web OCR. We compare the results based on characteristic and segmentation parameter. We define 0.5 and 1 point if web OCR recognizes the product price or/and names from product catalogs. Characteristic parameter is a parameter which identified product price and name using box li

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Athoillah, Muhammad. "K-Nearest Neighbor for Recognize Handwritten Arabic Character." Jurnal Matematika "MANTIK" 5, no. 2 (2019): 83–89. http://dx.doi.org/10.15642/mantik.2019.5.2.83-89.

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Handwritten text recognition is the ability of a system to recognize human handwritten and convert it into digital text. Handwritten text recognition is a form of classification problem, so a classification algorithm such as Nearest Neighbor (NN) is needed to solve it. NN algorithms is a simple algorithm yet provide a good result. In contrast with other algorithms that usually determined by some hypothesis class, NN Algorithm finds out a label on any test point without searching for a predictor within some predefined class of functions. Arabic is one of the most important languages in the worl

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Vijaya Lakshmi, T. R., P. Narahari Sastry, and T. V. Rajinikanth. "Feature selection to recognize text from palm leaf manuscripts." Signal, Image and Video Processing 12, no. 2 (2017): 223–29. http://dx.doi.org/10.1007/s11760-017-1149-9.

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Garg, Naresh Kumar, Lakhwinder Kaur, and M. K. Jindal. "Recognition of Handwritten Hindi Text Using Middle Region of the Words." International Journal of Software Innovation 3, no. 4 (2015): 62–71. http://dx.doi.org/10.4018/ijsi.2015100105.

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Offline handwritten Hindi text recognition is a very tedious task. In this paper, a novel method using middle region of the words for recognition of handwritten Hindi text is proposed. A segmentation based approach is used for recognition. Although many efforts have been made to recognize isolated characters and words, a little work has been done to recognize the offline handwritten Hindi text by segmenting the sentences into lines and lines into words. The uniqueness of this approach lies in the fact that many of the commonly used words can be recognized by matching all the characters in the

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Fadjeri, Akhmad, Atik Muhimatun Asroriyah, and Atiq Rahmawati. "Analisis Teks Bahasa Indonesia Dan Inggris Dari Sebuah Citra Menggunakan Pengolahan Citra Digital." Jurnal Teknologi Informasi dan Komunikasi (TIKomSiN) 10, no. 2 (2022): 42. http://dx.doi.org/10.30646/tikomsin.v10i2.650.

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The purpose of this research is to analyze Indonesian text and English text from images using digital image processing. The method used in this research is an experimental method such as: literature review , identification problems, hypotheses, analyzing materials, designing programs, conducting tests and drawing conclusions. The result of this research is to detect the text contained in the image. This research result a digital image processing program in text that is containing from an image that has text in it, using input or input in form of a character image. The image that will be recogn

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LECOMTE, DOMINIQUE. "HOW CAN WE RECOGNIZE POTENTIALLY ${\bf\Pi}^{0}_{\xi}$ SUBSETS OF THE PLANE?" Journal of Mathematical Logic 09, no. 01 (2009): 39–62. http://dx.doi.org/10.1142/s0219061309000793.

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Let ξ ≥ 1 be a countable ordinal. We study the Borel subsets of the plane that can be made [Formula: see text] by refining the Polish topology on the real line. These sets are called potentially [Formula: see text]. We give a Hurewicz-like test to recognize potentially [Formula: see text] sets.

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B. Kadam, Miss Poonam. "A Hybrid Approach To Detect And Recognize Text In Images." IOSR Journal of Engineering 4, no. 7 (2014): 13–19. http://dx.doi.org/10.9790/3021-04741319.

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Arthanugraha, Wahyu, Zahir Zainuddin, and Abdul Latief Arda. "The Implementation of Convolutional Neural Network in Recognize Lontara Text." Jurnal Informatika 11, no. 2 (2024): 65–72. http://dx.doi.org/10.31294/inf.v11i2.20328.

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Vijaya Lakshmi, T. R., Panyam Narahari Sastry, and T. V. Rajinikanth. "A novel 3D approach to recognize Telugu palm leaf text." Engineering Science and Technology, an International Journal 20, no. 1 (2017): 143–50. http://dx.doi.org/10.1016/j.jestch.2016.06.006.

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Mahavir, B., P. Roushini Leely Pushpam, and M. Kamalam. "An algorithm to recognize weak roman domination stable trees under vertex deletion." Discrete Mathematics, Algorithms and Applications 12, no. 04 (2020): 2050049. http://dx.doi.org/10.1142/s1793830920500494.

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Let [Formula: see text] be a graph and [Formula: see text] be a function. A vertex [Formula: see text] with weight [Formula: see text] is said to be undefended with respect to [Formula: see text], if it is not adjacent to any vertex with positive weight. The function [Formula: see text] is a weak Roman dominating function (WRDF) if each vertex [Formula: see text] with [Formula: see text] is adjacent to a vertex [Formula: see text] with [Formula: see text] such that the function [Formula: see text] defined by [Formula: see text], [Formula: see text] and [Formula: see text] if [Formula: see text

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Pramila, Shinde, Soni Niyoshi, Shrotriya Dhruwangi, and Shivhare Sahil. "Transcription of Text." Advancement in Image Processing and Pattern Recognition 5, no. 1 (2022): 1–8. https://doi.org/10.5281/zenodo.6451792.

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<em>Optical Character Recognition (OCR) is a technology that recognizes text in documents and converts it into an editable machine-readable format. Feature extraction, recognition, and classification into appropriate labels are the primary components of an OCR system. This paper uses image processing and (OCR) based architecture to segment, recognize, and identify documents. Moreover, handwriting has evolved, as evidenced by the different types of handwritten characters such as digit, numeral, cursive script, and symbols English and other languages. The automatic recognition of text can be an

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Tahir, Madiha, Zahid Halim, Atta Ur Rahman, et al. "Non-Acted Text and Keystrokes Database and Learning Methods to Recognize Emotions." ACM Transactions on Multimedia Computing, Communications, and Applications 18, no. 2 (2022): 1–24. http://dx.doi.org/10.1145/3480968.

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The modern computing applications are presently adapting to the convenient availability of huge and diverse data for making their pattern recognition methods smarter. Identification of dominant emotion solely based on the text data generated by humans is essential for the modern human–computer interaction. This work presents a multimodal text-keystrokes dataset and associated learning methods for the identification of human emotions hidden in small text. For this, a text-keystrokes data of 69 participants is collected in multiple scenarios. Stimuli are induced through videos in a controlled en

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Wu, V., R. Manmatha, and E. M. Riseman. "Textfinder: an automatic system to detect and recognize text in images." IEEE Transactions on Pattern Analysis and Machine Intelligence 21, no. 11 (1999): 1224–29. http://dx.doi.org/10.1109/34.809116.

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SRIDHARAN, Manikandan, Delphin Carolina RANI ARULANANDAM, Rajeswari K. CHINNASAMY, Suma THIMMANNA, and Sivabalaselvamani DHANDAPANI. "RECOGNITION OF FONT AND TAMIL LETTER IN IMAGES USING DEEP LEARNING." Applied Computer Science 17, no. 2 (2021): 90–99. http://dx.doi.org/10.35784/acs-2021-15.

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This paper proposes a deep learning approach to recognize Tamil Letter from images which contains text. This is recognition process, the text in the images are divided to letter or characters. Each recognized letters are sending to recognition system and filter the text using deep learning algorithms. Our proposed algorithm is used to separate letter from the text using convolution neural network approach. The filtering system is used for identifying font based on that letters are found. The Tamil letters are test data and loaded in recognition systems. The trained data are input which contain

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U, Chaitanya, Emmanuel Alisetti, Harsitha Ballam, and Maneesha Dodda. "Text Recognition from Images using CNN and MSER Algorithms." International Journal for Research in Applied Science and Engineering Technology 11, no. 6 (2023): 889–94. http://dx.doi.org/10.22214/ijraset.2023.53777.

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Abstract: The ability to recognize text from images is a great importance in a range of applications, including document analysis, images captioning and augmented reality. The reliability and accuracy of text extraction from images have been completely transformed by text recognition models using Optical Character Recognition (OCR) and Maximally Stable Extremal Regions (MSER) algorithms. In our study, we propose a text recognition model that leverages the advantages of both OCR and MSER algorithms to enhance the reliability and accuracy of the text extraction process. OCR algorithm serve as th

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Abhale, Prof Priyanka. "Text Summarization and Conversion of Speech to Text." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (2023): 6322–24. http://dx.doi.org/10.22214/ijraset.2023.52902.

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Abstract: This article describes the fusion of recurrent neural networks and deep learning algorithms for text summarization systems and analysis of the text learning process. Next, the text analytics learning model is summarized. In addition, applications of deep learning-based text analysis are also introduced. Language is the most important part of communication between people. Although there are many ways to express our thoughts and feelings, language is considered the most important medium of communication. Speech recognition is the process by which machines recognize different people's v

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Bamanikar, Prof Ashvini. "Phishing Attack Detection on Text Messages Using Machine Learning Algorithms." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (2023): 6285–90. http://dx.doi.org/10.22214/ijraset.2023.53177.

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Abstract: A revolutionary method that enables users to write in the air using hand gestures and precisely recognizes finger movement is the air writing and recognition system using the MediaPipe module. A new method that enables users to write in the air using hand movements while AWS Textract accurately translates and converts the written language is to use AWS Textract for air writing detection and recognition. It uses machine learning techniques to examine how the hand landmarks move over time and recognize various gestures. The system is an effective and user-friendly solution for jobs req

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Kang, In-Su. "Using Collective Citing Sentences to Recognize Cited Text in Computational Linguistics Articles." Journal of the Korea Society of Computer and Information 21, no. 11 (2016): 85–91. http://dx.doi.org/10.9708/jksci.2016.21.11.085.

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Mustafa, Nada Abdul Aziz. "Text hiding in text using invisible character." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 4 (2020): 3550. http://dx.doi.org/10.11591/ijece.v10i4.pp3550-3557.

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Steganography can be defined as the art and science of hiding information in the data that could be read by computer. This science cannot recognize stego-cover and the original one whether by eye or by computer when seeing the statistical samples. This paper presents a new method to hide text in text characters. The systematic method uses the structure of invisible character to hide and extract secret texts. The creation of secret message comprises four main stages such using the letter from the original message, selecting the suitable cover text, dividing the cover text into blocks, hiding th

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Nada, Abdul Aziz Mustafa. "Text hiding in text using invisible character." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 4 (2020): 3550–57. https://doi.org/10.11591/ijece.v10i4.pp3550-3557.

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Steganography can be defined as the art and science of hiding information in the data that could be read by computer. This science cannot recognize stego-cover and the original one whether by eye or by computer when seeing the statistical samples. This paper presents a new method to hide text in text characters. The systematic method uses the structure of invisible character to hide and extract secret texts. The creation of secret message comprises four main stages such using the letter from the original message, selecting the suitable cover text, dividing the cover text into blocks, hiding th

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Devaraj, Anjali Yogesh, Anup S. Jain, Omisha N, and Shobana TS. "Kannada Text Recognition." International Journal for Research in Applied Science and Engineering Technology 10, no. 9 (2022): 73–78. http://dx.doi.org/10.22214/ijraset.2022.46520.

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Abstract: The task of automatic handwriting recognition is critical. This can be a difficult subject, and it has gotten a lot of attention in recent years. In the realm of picture grouping, handwritten character recognition is a problem. Handwritten characters are difficult to decipher since various people have distinct handwriting styles. For decades, researchers have been focusing on character identification in Latin handwriting. Kannada has had fewer studies conducted on it. Our "Kannada Text Recognition" research and effort attempts to classify and recognize characters written in Kannada,

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Thobhani, Alaa, Mingsheng Gao, Ammar Hawbani, Safwan Taher Mohammed Ali, and Amr Abdussalam. "CAPTCHA Recognition Using Deep Learning with Attached Binary Images." Electronics 9, no. 9 (2020): 1522. http://dx.doi.org/10.3390/electronics9091522.

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Websites can increase their security and prevent harmful Internet attacks by providing CAPTCHA verification for determining whether end-user is a human or a robot. Text-based CAPTCHA is the most common and designed to be easily recognized by humans and difficult to identify by machines or robots. However, with the dramatic advancements in deep learning, it becomes much easier to build convolutional neural network (CNN) models that can efficiently recognize text-based CAPTCHAs. In this study, we introduce an efficient CNN model that uses attached binary images to recognize CAPTCHAs. By making a

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Kasthuri, A. Meena, and Mr Barath Kesavan. "Advanced Blind helper Android Application Using Text-to-speech synthesis." International Journal for Research in Applied Science and Engineering Technology 11, no. 3 (2023): 1951–54. http://dx.doi.org/10.22214/ijraset.2023.49861.

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Abstract: This project aims to develop an Android application that enables blind people to detect currency denominations and make phone calls using handwritten text recognition and voice commands. The application uses image processing techniques and a trained TensorFlow model to recognize the denomination of banknotes, and an ML model to recognize handwritten text and convert it to a phone number. The user can initiate a phone call by saying a voice command, and the application provides audio feedback throughout the process.

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Snitko, Marianna D., Iana V. Khitsko, and Nataliia A. Rybachok. "Recognition of Handwritten Texts on Images Using Deep Machine Learning." Control Systems and Computers, no. 1 (305) (2024): 50–56. http://dx.doi.org/10.15407/csc.2024.01.050.

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The article is devoted to the aspects of using deep machine learning to recognize handwritten text containing letters of the Latin alphabet and numbers. Software has been developed that recognizes handwritten text. A convolutional neural network consisting of 13 layers was trained for 50 epochs on images of 814255 characters taken from the EMNIST dataset. The prediction accuracy was 0.9468, the response rate was 0,9673, the F1-index reached 0,9429, and the average processing time of one image was 1,15 seconds.

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Haque, S. M. Anamul, Shahida Arbi, Tabassum Tamanna, and Sadia Mahsina Itu. "Automatic Detection and Translation of Bengali Text on Road Sign for Visually Impaired." DIU Journal of Science & Technology 2, no. 2 (2024): 1–7. https://doi.org/10.5281/zenodo.13683062.

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Large amount of information are embedded in natural scenes. Signs are good example of natural objects with high information content. Visually impaired individuals are unable to utilize the significant amount of information in signs. This paper presents a system for detecting and recognizing the signs in the environment specially written in Bengali and voicesynthesizing their contents. In this paper we present an algorithm for detection and localization of text in road sign using edge detection approach. We use an adaptive thresholding method to binarize the text blocks and recognize the text u

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Ravikumar, Hodikehosahally Channegowda, Karthik Palani, Srinivasaiah Raghavendra, and Shivaraj Mahadev. "Customized mask region based convolutional neural networks for un-uniformed shape text detection and text recognition." International Journal of Electrical and Computer Engineering (IJECE) 13, no. 1 (2023): 413–24. https://doi.org/10.11591/ijece.v13i1.pp413-424.

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In image scene, text contains high-level of important information that helps to analyze and consider the particular environment. In this paper, we adapt image mask and original identification of the mask region based convolutional neural networks (R-CNN) to allow recognition at 3 levels such as sequence, holistic and pixel-level semantics. Particularly, pixel and holistic level semantics can be utilized to recognize the texts and define the text shapes, respectively. Precisely, in mask and detection, we segment and recognize both character and word instances. Furthermore, we implement text det

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Zhang, Liang, Shu Guang Huang, Rong Gui Hu, He Ping Tang, and Zhao Xiang Shi. "A Novel Method to Recognise Closely Connected CAPTCHA." Advanced Materials Research 457-458 (January 2012): 620–27. http://dx.doi.org/10.4028/www.scientific.net/amr.457-458.620.

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Nowadays, most of text CAPTCHAs use character connection as a primary means to avoid being recognized. To recognize this kind of CAPTCHA, a new image analysis model, Concept Component Analysis (CCA), is proposed. Based on the approaching idea in Newton’s iteration, this new model is solved by a multi-population genetic algorithm. Compared with traditional image analysis models, such as PCA, components obtained by CCA have obvious concept meanings. Images can be recognized by solely relying on these components, No classifier is needed. CCA has achieved good recognition results in our experiment

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Joshi, Kalpesh. "Handwritten Text Recognition from Image." International Journal for Research in Applied Science and Engineering Technology 11, no. 6 (2023): 1528–30. http://dx.doi.org/10.22214/ijraset.2023.53364.

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Abstract: A computer vision program called Handwritten Text Recognition (HTR) attempts to recognize and translate handwritten text from scanned or photographed images. In this project, we suggest implementing an HTR system using Tesseract and OpenCV. English, Chinese, and Arabic are all supported by the popular open-source optical character recognition (OCR) engine known as Tesseract. It is employed to find and identify printed text within photographs. On the other hand, OpenCV is a well-liked computer vision library that offers several tools for processing and analyzing images. The pre-proces

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Channegowda, Ravikumar Hodikehosahally, Palani Karthik, Raghavendra Srinivasaiah, and Mahadev Shivaraj. "Customized mask region based convolutional neural networks for un-uniformed shape text detection and text recognition." International Journal of Electrical and Computer Engineering (IJECE) 13, no. 1 (2023): 413. http://dx.doi.org/10.11591/ijece.v13i1.pp413-424.

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<p><span lang="EN-US">In image scene, text contains high-level of important information that helps to analyze and consider the particular environment. In this paper, we adapt image mask and original identification of the mask region based convolutional neural networks (R-CNN) to allow recognition at 3 levels such as sequence, holistic and pixel-level semantics. Particularly, pixel and holistic level semantics can be utilized to recognize the texts and define the text shapes, respectively. Precisely, in mask and detection, we segment and recognize both character and word instances.

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Smith, Therese F., and Amos L. Hahn. "Intermediate-Grade Students' Sensitivity to Macrostructure Intrusions." Journal of Reading Behavior 21, no. 2 (1989): 167–80. http://dx.doi.org/10.1080/10862968909547668.

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Students' sensitivity to four top-level text structures was investigated using a methodology not used previously in this area of research—the error-detection paradigm. Forty-eight students in Grades 4, 6, and 8 read short paragraphs organized according to the following text structures: compare/contrast, description, enumeration, and sequence. An intrusion sentence which signalled an enumeration text structure was inserted in the compare/contrast, description, and sequence paragraphs. The enumeration paragraphs did not contain any intrusive text-structure information. Following the reading of e

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Wang, Yinuo, Songbiao Zhu, Chengcheng Liu, Haiteng Deng, and Zhenyu Zhang. "Text Mining and Hub Gene Network Analysis of Endometriosis." BioMed Research International 2021 (December 7, 2021): 1–10. http://dx.doi.org/10.1155/2021/5517145.

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This study is aimed at systematically characterizing the endometriosis-associated genes based on text mining and at annotating the functions, pathways, and networks of endometriosis-associated hub genes. We extracted endometriosis-associated abstracts published between 1970 and 2020 from the PubMed database. A neural-named entity recognition and multitype normalization tool for biomedical text mining was used to recognize and normalize the genes and proteins embedded in the abstracts. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses were conducted to annota

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Knapton, Olivia, and Gabriella Rundblad. "Metaphor, discourse dynamics and register: applications to written descriptions of mental health problems." Text & Talk 38, no. 3 (2018): 389–410. http://dx.doi.org/10.1515/text-2018-0005.

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Abstract Discursive approaches to metaphor recognize that different social contexts and discourse activities will influence metaphor use. Using a descriptive text written by a participant with obsessive-compulsive disorder (OCD) as a case study, we demonstrate how, in a research context, metaphors do not only serve a representational function but they can also build relationships between the researcher and the participant, create a persuasive piece of writing and construct multiple identities. Through an analysis of metaphors and their surrounding, non-metaphorical co-text, it is thus argued t

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Zhang, Fan, Jiaxing Luan, Zhichao Xu, and Wei Chen. "DetReco: Object-Text Detection and Recognition Based on Deep Neural Network." Mathematical Problems in Engineering 2020 (July 14, 2020): 1–15. http://dx.doi.org/10.1155/2020/2365076.

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Deep learning-based object detection method has been applied in various fields, such as ITS (intelligent transportation systems) and ADS (autonomous driving systems). Meanwhile, text detection and recognition in different scenes have also attracted much attention and research effort. In this article, we propose a new object-text detection and recognition method termed “DetReco” to detect objects and texts and recognize the text contents. The proposed method is composed of object-text detection network and text recognition network. YOLOv3 is used as the algorithm for the object-text detection t

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Nyein, Nyein Oo, Yamashita Masaru, and Matsunaga Shoichi. "Normal and Whispered Speech Recognition Systems for Myanmar Digits." International Journal of Science and Engineering Applications Volume 7, Issue 11 (2018): 465–69. https://doi.org/10.5281/zenodo.2620096.

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Nowadays, Automatic speech recognition (ASR) technology comes as the popular innovation in human machine interaction. This technology allows a computer to recognize the spoken words and convert them to text data. In designing the computer systems that recognize spoken words, one of the challenging tasks is to be recognized spoken Myanmar digits. In this paper we focus on recognizing Myanmar digits spoken by normal voice and whispered voice. Myanmar digits recognition system for both types has been developed by using Hidden Markov Model in HTK tools and Mel Frequency Cepstral Coefficients (MFCC

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Gupta, Ashim, Giorgi Kvernadze, and Vivek Srikumar. "BERT & Family Eat Word Salad: Experiments with Text Understanding." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 14 (2021): 12946–54. http://dx.doi.org/10.1609/aaai.v35i14.17531.

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In this paper, we study the response of large models from the BERT family to incoherent inputs that should confuse any model that claims to understand natural language. We define simple heuristics to construct such examples. Our experiments show that state-of-the-art models consistently fail to recognize them as ill-formed, and instead produce high confidence predictions on them. As a consequence of this phenomenon, models trained on sentences with randomly permuted word order perform close to state-of-the-art models. To alleviate these issues, we show that if models are explicitly trained to

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Kumar, Dr Girish. "Text Recognition Using Silent Speech." International Journal for Research in Applied Science and Engineering Technology 9, no. VIII (2021): 556–59. http://dx.doi.org/10.22214/ijraset.2021.37414.

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Our objective is to identify the characters from the quite speech of the English language. We tend to focus on the lip region to recognize the characters spoken clearly in the video. Our contribution is: foremost, this model is developed by using a pipeline method form absolutely automatic information assortment from the video. Though this, it generates a data set that is spoken by the individuals. Secondly, it is developed by using the machine learning algorithm Convolution Neural Network (CNN) that learns the lip motion. Thirdly, Convolution network turn out the efficient result by examining

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Kamble, Akash. "Conversion of Sign Language to Text." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (2023): 1963–68. http://dx.doi.org/10.22214/ijraset.2023.51981.

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Abstract: Sign language is a form of communication that uses hand sign and gestures to convey meaning. we present a new approach to converting sign language into text format. Our system is designed to enable deaf and mute people to communicate with others in a more accessible and convenient way. The proposed method uses computer vision and deep learning methods to recognize hand gestures and translate them into appropriate text. The system was built using a combination of key point detection using MediaPipe, data pre-processing, label, feature generation and LSTM neural network training. This

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Önal, Erhan, and Noe Lopez-Benitez. "A MULTI-LINGUAL TRANSLATION SYSTEM FOR REAL-WORLD IMAGES." Journal of Integrated Design and Process Science: Transactions of the SDPS, Official Journal of the Society for Design and Process Science 10, no. 1 (2006): 17–30. http://dx.doi.org/10.3233/jid-2006-10102.

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Translating text in real-world images presents several challenges such as text detection, text extraction, recognition, and translation. A multi-lingual translation system must take fundamental differences between the characteristics of different alphabets such as Latin, Cyrillic, Chinese, Korean, and Arabic into account. The system presented in this paper can extract text from real-world images with appropriate heuristics for these alphabets, as well as de-skew, binarize, recognize, and translate them. OCR is utilized to recognize the text, and the translation is employed using Translation Me

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P, Golda Jeyasheeli, Athinarayanan B, Manish T, and Mohamad Umar M. "Scene Text Detection and Recognition Using Maximally Stable Extremal Region." Journal of Applied Engineering and Technological Science (JAETS) 6, no. 1 (2024): 103–14. https://doi.org/10.37385/jaets.v6i1.5958.

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In recent years, scene text detection and recognition have become important research areas in computer vision and machine learning. Traditional text detection and recognition methods may struggle with detecting and recognizing text in images with low resolution, complex backgrounds, and varying font sizes. The proposed methodology addresses these challenges by combining multiple algorithms and using deep learning techniques. In this paper, we propose a method for scene text detection based on Maximally Stable Extremal Regions (MSER) combined with Stroke Width Transform (SWT) and recognition us

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Linse, Caroline, and Stephen van Vlack. "Plain English." ITL - International Journal of Applied Linguistics 166, no. 2 (2015): 254–77. http://dx.doi.org/10.1075/itl.166.2.03lin.

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Schools attempting to engage with the families of all learners, including those with culturally and linguistically diverse backgrounds recognize the importance of oral and written communication that is written using understandable English. Using the basic principles of plain English coupled with speech act theory we began to investigate different aspects of text and functional intent. This exploratory research examined exemplar pieces of written school generated communication, using different forms of linguistic analysis to determine whether the communication contained elements recognized to f

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Yi, Moung Ho, Myung Jin Lim, and Ju Hyun Shin. "Multi-Emotion Recognition Model with Text and Speech Ensemble." Korean Institute of Smart Media 11, no. 8 (2022): 65–72. http://dx.doi.org/10.30693/smj.2022.11.8.65.

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Due to COVID-19, the importance of non-face-to-face counseling is increasing as the face-to-face counseling method has progressed to non-face-to-face counseling. The advantage of non-face-to-face counseling is that it can be consulted online anytime, anywhere and is safe from COVID-19. However, it is difficult to understand the client's mind because it is difficult to communicate with non-verbal expressions. Therefore, it is important to recognize emotions by accurately analyzing text and voice in order to understand the client's mind well during non-face-to-face counseling. Therefore, in this

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