Relevant bibliographies by topics / Real-time Text Recognition

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Real-time Text Recognition'

Author: Grafiati
Published: 25 June 2021
Last updated: 15 February 2022

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Journal articles on the topic "Real-time Text Recognition"

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Neumann, Lukas, and Jiri Matas. "Real-Time Lexicon-Free Scene Text Localization and Recognition." IEEE Transactions on Pattern Analysis and Machine Intelligence 38, no. 9 (September 1, 2016): 1872–85. http://dx.doi.org/10.1109/tpami.2015.2496234.

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Merino‐Gracia, Carlos, and Majid Mirmehdi. "Real‐time text tracking in natural scenes." IET Computer Vision 8, no. 6 (December 2014): 670–81. http://dx.doi.org/10.1049/iet-cvi.2013.0217.

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Thakur, Amrita, Pujan Budhathoki, Sarmila Upreti, Shirish Shrestha, and Subarna Shakya. "Real Time Sign Language Recognition and Speech Generation." Journal of Innovative Image Processing 2, no. 2 (June 3, 2020): 65–76. http://dx.doi.org/10.36548/jiip.2020.2.001.

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Sign Language is the method of communication of deaf and dumb people all over the world. However, it has always been a difficulty in communication between a verbal impaired person and a normal person. Sign Language Recognition is a breakthrough for helping deaf-mute people to communicate with others. The commercialization of an economical and accurate recognition system is today’s concern of researchers all over the world. Thus, sign language recognition systems based on Image processing and neural networks are preferred over gadget system as they are more accurate and easier to make. The aim of this paper is to build a user friendly and accurate sign language recognition system trained by neural network thereby generating text and speech of the input gesture. This paper also presents text to sign language generation model that enables a way to establish a two-way communication without the need of a translator.
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Taha, Mohamed, Noha Abd-ElKareem, and Mazen Selim. "Real-Time Arabic Text-Reading for Visually Impaired People." International Journal of Sociotechnology and Knowledge Development 13, no. 2 (April 2021): 168–85. http://dx.doi.org/10.4018/ijskd.2021040110.

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Visually impaired (VI) people suffer from many difficulties when accessing printed material using existing technologies. These problems may include text alignment, focus, accuracy, software processing speed, mobility, and efficiency. Current technologies such as flatbed scanners and OCR programs need to scan an entire page. Recently, VI people prefer mobile devices because of their handiness and accessibility, but they have problems with focusing the mobile camera on the printed material. In this paper, a real-time Arabic text-reading prototype for VI people is proposed. It is based on using a wearable device for a hand finger. It is designed as a wearable ring attached to a tiny webcam device. The attached camera captures the printed Arabic text and passes it to the Arabic OCR system. Finally, the recognized characters are translated into speech using the text-to-speech (TTS) technology. Experimental results demonstrate the feasibility of the proposed prototype. It achieved an accuracy of 95.86% for Arabic character recognition and 98.5% for English character recognition.
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Mafla, Andrés, Rubèn Tito, Sounak Dey, Lluís Gómez, Marçal Rusiñol, Ernest Valveny, and Dimosthenis Karatzas. "Real-time Lexicon-free Scene Text Retrieval." Pattern Recognition 110 (February 2021): 107656. http://dx.doi.org/10.1016/j.patcog.2020.107656.

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Al-Jumaily, Harith, Paloma Martínez, José L. Martínez-Fernández, and Erik Van der Goot. "A real time Named Entity Recognition system for Arabic text mining." Language Resources and Evaluation 46, no. 4 (May 1, 2011): 543–63. http://dx.doi.org/10.1007/s10579-011-9146-z.

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Choi, Yong-Sik, Jin-Gu Kang, Jong Wha J. Joo, and Jin-Woo Jung. "Real-time Informatized caption enhancement based on speaker pronunciation time database." Multimedia Tools and Applications 79, no. 47-48 (September 5, 2020): 35667–88. http://dx.doi.org/10.1007/s11042-020-09590-2.

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AbstractIBM Watson is one of the representative tools for speech recognition system which can automatically generate not only speech-to-text information but also speaker ID and timing information, which is called as Informatized Caption. However, if there is some noise in the voice signal to the IBM Watson API, the recognition performance is significantly decreased. It can be easily found in movies with background music and special sound effects. This paper aims to improve the inaccuracy problem of current Informatized Captions in noisy environments. In this paper, a method of modifying incorrectly recognized words and a method of enhancing timing accuracy while updating database in real time are suggested based on the original caption and Informatized Caption information. Experimental results shows that the proposed method can give 81.09% timing accuracy for the case of 10 representative animation, horror and action movies.
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Lu, Zhiyuan, Xiang Chen, Xu Zhang, Kay-Yu Tong, and Ping Zhou. "Real-Time Control of an Exoskeleton Hand Robot with Myoelectric Pattern Recognition." International Journal of Neural Systems 27, no. 05 (May 3, 2017): 1750009. http://dx.doi.org/10.1142/s0129065717500095.

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Robot-assisted training provides an effective approach to neurological injury rehabilitation. To meet the challenge of hand rehabilitation after neurological injuries, this study presents an advanced myoelectric pattern recognition scheme for real-time intention-driven control of a hand exoskeleton. The developed scheme detects and recognizes user’s intention of six different hand motions using four channels of surface electromyography (EMG) signals acquired from the forearm and hand muscles, and then drives the exoskeleton to assist the user accomplish the intended motion. The system was tested with eight neurologically intact subjects and two individuals with spinal cord injury (SCI). The overall control accuracy was [Formula: see text] for the neurologically intact subjects and [Formula: see text] for the SCI subjects. The total lag of the system was approximately 250[Formula: see text]ms including data acquisition, transmission and processing. One SCI subject also participated in training sessions in his second and third visits. Both the control accuracy and efficiency tended to improve. These results show great potential for applying the advanced myoelectric pattern recognition control of the wearable robotic hand system toward improving hand function after neurological injuries.
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Zhan, Ce, Wanqing Li, Philip Ogunbona, and Farzad Safaei. "A Real-Time Facial Expression Recognition System for Online Games." International Journal of Computer Games Technology 2008 (2008): 1–7. http://dx.doi.org/10.1155/2008/542918.

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Multiplayer online games (MOGs) have become increasingly popular because of the opportunity they provide for collaboration, communication, and interaction. However, compared with ordinary human communication, MOG still has several limitations, especially in communication using facial expressions. Although detailed facial animation has already been achieved in a number of MOGs, players have to use text commands to control the expressions of avatars. In this paper, we propose an automatic expression recognition system that can be integrated into an MOG to control the facial expressions of avatars. To meet the specific requirements of such a system, a number of algorithms are studied, improved, and extended. In particular, Viola and Jones face-detection method is extended to detect small-scale key facial components; and fixed facial landmarks are used to reduce the computational load with little performance degradation in the recognition accuracy.
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Oliveira-Neto, Francisco Moraes, Lee D. Han, and Myong K. Jeong. "Tracking Large Trucks in Real Time with License Plate Recognition and Text-Mining Techniques." Transportation Research Record: Journal of the Transportation Research Board 2121, no. 1 (January 2009): 121–27. http://dx.doi.org/10.3141/2121-13.

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Dissertations / Theses on the topic "Real-time Text Recognition"

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Gunaydin, Ali Gokay. "A Constraint Based Real-time License Plate Recognition System." Master's thesis, METU, 2007. http://etd.lib.metu.edu.tr/upload/12608195/index.pdf.

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License Plate Recognition (LPR) systems are frequently utilized in various access controls and security applications. In this thesis, an experimental constraint based real-time License Plate Recognition system is designed, and implemented in Java platform. Many of the available constraint based methods worked under strict restrictions such as plate color, fixed illumination and designated routes, whereas, only the license plate geometry and format constraints are used in this developed system. These constraints are built on top of the current Turkish license plate regulations. The plate localization algorithm is based on vertical edge features where constraints are used to filter out non-text regions. Vertical and horizontal projections are used for character segmentation and Multi Layered Perceptron (MLP) based Optical Character Recognition (OCR) module has been implemented for character identification. The extracted license plate characters are validated against possible license plate formats during the recognition process. The system is tested both with Turkish and foreign license plate images including various plate orientation, image quality and size. An accuracy of 92% is achieved for license plate localization and %88 for character segmentation and recognition.
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Raymondi, Luis Guillermo Antezana, Fabricio Eduardo Aguirre Guzman, Jimmy Armas-Aguirre, and Paola Agonzalez. "Technological solution for the identification and reduction of stress level using wearables." IEEE Computer Society, 2020. http://hdl.handle.net/10757/656578.

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El texto completo de este trabajo no está disponible en el Repositorio Académico UPC por restricciones de la casa editorial donde ha sido publicado.
In this article, a technological solution is proposed to identify and reduce the level of mental stress of a person through a wearable device. The proposal identifies a physiological variable: Heart rate, through the integration between a wearable and a mobile application through text recognition using the back camera of a smartphone. As part of the process, the technological solution shows a list of guidelines depending on the level of stress obtained in a given time. Once completed, it can be measured again in order to confirm the evolution of your stress level. This proposal allows the patient to keep his stress level under control in an effective and accessible way in real time. The proposal consists of four phases: 1. Collection of parameters through the wearable; 2. Data reception by the mobile application; 3. Data storage in a cloud environment and 4. Data collection and processing; this last phase is divided into 4 sub-phases: 4.1. Stress level analysis, 4.2. Recommendations to decrease the level obtained, 4.3. Comparison between measurements and 4.4. Measurement history per day. The proposal was validated in a workplace with people from 20 to 35 years old located in Lima, Peru. Preliminary results showed that 80% of patients managed to reduce their stress level with the proposed solution.
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Hsieh, Yi-Chia, and 謝易家. "Real-Time Scene Text Detection and Recognition Using Extremal Region." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/f442q9.

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碩士
國立臺灣科技大學
電機工程系
105
In the era of information explosion, multimedia has become an indispensable part of modern life. People use videos and images as digital diary and create enormous image text data consequently. Texts in image usually contain informative data, and therefore scene text recognition system would be a promising application. This thesis presents a fast scene text localization and recognition algorithm. We have develop a system that takes images as input and recognizes texts in the input images as output. The system consists of three parts: (1) Character candidate extraction, (2) Character classification and grouping, (3) Optical character recognition. In the first stage, extremal region(ER) is used as a candidate extractor. In order to reach high recall rate, we extract ER in multiple channels such as YCrCb and their inverted channels. A non-maximum suppression skill is introduced to eliminate overlapped candidates. In the second stage, we used mean local binary pattern as feature and train our classifier by AdaBoost. Text candidates are classified as one of strong text, weak text and non-text by a 2 stages classifier. The 2 stages classifier is intended to remain high recall and precision simultaneously. We then track the weak texts with strong texts as long as they have similar properties. Our next step was to group the candidates and transform them from character level to word level. Finally, our optical character recognition is done by using chain-code direction as feature and support vector machine as classifier. The experimental results show that our system is able to detect text in real-time and recognize text in nearly real-time. In addition, the system can detect text in different text fonts and text size, also tolerate moderate rotation, blurring and inconsistent lighting. Thus, the robustness of the system is validated.
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Wong, Wei-Hong, and 翁瑋鴻. "A Mobile App for Real-time Text Recognition Based on WEB OCR Engine." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/21161004072031633863.

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碩士
國立高雄第一科技大學
電腦與通訊工程研究所
101
As the smart phone and network links application and popularization, the user life also changed. In addition to telephone functions ,smart phone features like a small computer , many of the computer''s functions are also included, therefore, let many users for smart phones become dependent on more and more. Nowadays many mobile application design for user aspects in order to achieve "Software is the service" concept of the demand for life. In this thesis, the proposed mobile App is implemented in the smart phones based on the Android platform . First, the image of target text is captured by the camera function and then to upload the image to the Web OCR text real-time system replace the traditional form of keys to get the message with WI-FI/3G network function .Finally, the relative information of the image is sended back to the smart phone. Hoped that the technology of mobile applications in this research that can bring users more convenient life.
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Oliveira, Neto Francisco Moraes. "Matching Vehicle License Plate Numbers Using License Plate Recognition and Text Mining Techniques." 2010. http://trace.tennessee.edu/utk_graddiss/836.

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License plate recognition (LPR) technology has been widely applied in many different transportation applications such as enforcement, vehicle monitoring and access control. In most applications involving enforcement (e.g. cashless toll collection, congestion charging) and access control (e.g. car parking) a plate is recognized at one location (or checkpoint) and compared against a list of authorized vehicles. In this research I dealt with applications where a vehicle is detected at two locations and there is no list of reference for vehicle identification. There seems to be very little effort in the past to exploit all information generated by LPR systems. In nowadays, LPR machines have the ability to recognize most characters on the vehicle plates even under the harshest practical conditions. Therefore, even though the equipment are not perfect in terms of plate reading, it is still possible to judge with certain confidence if a pair of imperfect readings, in the form of sequenced characters (strings), most likely belong to the same vehicle. The challenge here is to design a matching procedure in order to decide whether or not they belong to same vehicle. In view of the aforementioned problem, this research intended to design and assess a matching procedure that takes advantage of a similarity measure called edit distance (ED) between two strings. The ED measure the minimum editing cost to convert a string to another. The study first attempted to assess a simple case of a dual LPR setup using the traditional ED formulation with 0 or 1 cost assignments (i.e. 0 if a pair-wise character is the same, and 1 otherwise). For this dual setup, this research has further proposed a symbol-based weight function using a probabilistic approach having as input parameters the conditional probability matrix of character association. As a result, this new formulation outperformed the original ED formulation. Lastly, the research sought to incorporate the passage time information into the procedure. With this, the performance of the matching procedure improved considerably resulting in a high positive matching rate and much lower (about 2%) false matching rate.
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Cheng, I.-Hua, and 鄭宜樺. "Application of 3D Coordinate and Real-time Character Recognition for 5DoF Robotic Arm on Smartphone Automatic Test System." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/14563813731636057753.

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碩士
國立臺灣海洋大學
通訊與導航工程學系
103
In this study three webcams are applied to a 5DoF robotic arm system that is applied to smartphone testing operation. One of the cameras is used to recognize words and numbers from the control panel by the use of Optical Character Recognize (OCR) and pattern matching process; it is an indication or command sending to human's brain for decision. In here the computer is the robot's brain that receives the command and executes decision. Another two cameras are used for catching the left and right images for 3D coordinates of object, and they are similar to human's eyes that can tell the position of object. We can easily see that the robotic arm system can catch the 3D coordinates of object and perform testing operations by the command from visual recognition. In the first step, we need to process the calibration procedure and get the relative internal/external parameters by two webcams. Then the values from image plan can be compared and transformed to 3D coordinates by the Q matrix. The coordinates can be translated to 4096 precision values in robotic arm system. In here we also use the inverse kinematics and translation between pixels and distance in the real world to check the relative position and further to execute the tested smartphone functions. In control panel, for receiving command, we use another webcam to catch the message from the monitor of the control PC by the OCR and pattern matching process. The words of command can be obtained after image translation from RGB to HSL color space. The message will then be sent to robotic arm. Movements of the robotic arm are based on fuzzy logic theory that can drive the robot arm to the relative point and position of object. The robot will execute operations that are requested. The feedback values of arm movement are applied to correct the position error in real time.
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Book chapters on the topic "Real-time Text Recognition"

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Nouza, Jan. "A Large Czech Vocabulary Recognition System for Real-Time Applications." In Text, Speech and Dialogue, 217–22. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/3-540-45323-7_37.

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Nouza, Jan. "Strategies for Developing a Real-Time Continuous Speech Recognition System for Czech Language." In Text, Speech and Dialogue, 189–96. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-46154-x_26.

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RajithKumar, B. K., H. S. Mohana, Divya A. Jamakhandi, K. V. Akshatha, Disha B. Hegde, and Amisha Singh. "Real-Time Input Text Recognition System for the Aid of Visually Impaired." In Proceedings of the International Conference on ISMAC in Computational Vision and Bio-Engineering 2018 (ISMAC-CVB), 147–57. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-00665-5_16.

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Yoshihashi, Ryota, Tomohiro Tanaka, Kenji Doi, Takumi Fujino, and Naoaki Yamashita. "Context-Free TextSpotter for Real-Time and Mobile End-to-End Text Detection and Recognition." In Document Analysis and Recognition – ICDAR 2021, 240–57. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-86331-9_16.

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Kumar, Sandeep, Sanjana Mathew, Navya Anumula, and K. Shravya Chandra. "Portable Camera-Based Assistive Device for Real-Time Text Recognition on Various Products and Speech Using Android for Blind People." In Lecture Notes in Networks and Systems, 437–48. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-3172-9_42.

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Rege, Priti P., and Shaheera Akhter. "Text Separation From Document Images." In Machine Learning and Deep Learning in Real-Time Applications, 283–313. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-3095-5.ch013.

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Text separation in document image analysis is an important preprocessing step before executing an optical character recognition (OCR) task. It is necessary to improve the accuracy of an OCR system. Traditionally, for separating text from a document, different feature extraction processes have been used that require handcrafting of the features. However, deep learning-based methods are excellent feature extractors that learn features from the training data automatically. Deep learning gives state-of-the-art results on various computer vision, image classification, segmentation, image captioning, object detection, and recognition tasks. This chapter compares various traditional as well as deep-learning techniques and uses a semantic segmentation method for separating text from Devanagari document images using U-Net and ResU-Net models. These models are further fine-tuned for transfer learning to get more precise results. The final results show that deep learning methods give more accurate results compared with conventional methods of image processing for Devanagari text extraction.
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R, Kedar, Kaviraj A, Manish R, Niteesh B, and Suthir S. "Authorized Vehicle Recognition System." In Intelligent Systems and Computer Technology. IOS Press, 2020. http://dx.doi.org/10.3233/apc200190.

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The technology is growing and increasing in our day to day life to satisfy the needs of human beings. The system we are going to propose makes the human job easier. Here the YOLO algorithm which is a deep learning object detection architecture is used to detect the number plate of the vehicle. After detecting the number plate it converts the vehicle number to a text format. Then it checks it with the database to see if the vehicle is authorized to enter into the premise or not. This system can be implemented in highly restrained areas such as military areas, government organizations, Parliament, etc. This proposed system has around six stages: Capture Image, Search for black pixels, Image filtering, Plate region extraction, character extraction, OCR for character recognition. The alphanumeric characters are identified using the OCR algorithm. It is then used to compare the obtained result from the YOLO algorithm with the database and then check if the vehicle is allowed to enter the premise or not. This proposed system is simulated and implemented using Python, and it was also tested on real-time images for performance purposes.
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Tarasconi, Francesco, Milad Botros, Matteo Caserio, Gianpiero Sportelli, Giuseppe Giacalone, Carlotta Uttini, Luca Vignati, and Fabrizio Zanetta. "Natural Language Processing Applications in Case-Law Text Publishing." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2020. http://dx.doi.org/10.3233/faia200859.

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Processing case-law contents for electronic publishing purposes is a time-consuming activity that encompasses several sub-tasks and usually involves adding annotations to the original text. On the other hand, recent trends in Artificial Intelligence and Natural Language Processing enable the automatic and efficient analysis of big textual data. In this paper we present our Machine Learning solution to three specific business problems, regularly met by a real world Italian publisher in their day-to-day work: recognition of legal references in text spans, new content ranking by relevance, and text classification according to a given tree of topics. Different approaches based on BERT language model were experimented with, together with alternatives, typically based on Bag-of-Words. The optimal solution, deployed in a controlled production environment, was in two out of three cases based on fine-tuned BERT (for the extraction of legal references and text classification), while, in the case of relevance ranking, a Random Forest model, with hand-crafted features, was preferred. We will conclude by discussing the concrete impact, as perceived by the publisher, of the developed prototypes.
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Sarma, Minerva, Anuskha Kumar, Aditi Joshi, Suraj Kumar Nayak, and Biswajeet Champaty. "Development of a Text-to-Speech Scanner for Visually Impaired People." In Advances in Medical Technologies and Clinical Practice, 218–38. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-4969-7.ch010.

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In this chapter, a low-cost, efficient, and real-time wearable text-to-speech scanner has been proposed that can enable blind persons to hear the contents of a text material. The device captures the images of the text and converts them to speech. The hardware of the device has been realized using Raspberry Pi 3, Pi camera, and an earphone. Optical character recognition (OCR) and text-to-speech synthesis (TTS) have been implemented using Raspberry Pi 3 to accomplish the working of the device. OCR technology converted the captured text images to editable text, whereas the TTS technology scanned the alphanumeric characters in the processed image and converted them to speech. The proposed technology imitates the ability of the human sensory organs and the nervous system, where the camera mimics human eye and the image processing in Raspberry Pi 3 substitutes the human brain. This proposed device can also help people suffering from diseases like dyslexia and nyctalopia, and inability to see in dim light or at night.
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West-Eberhard, Mary Jane. "Phenotypic Recombination Due to Learning." In Developmental Plasticity and Evolution. Oxford University Press, 2003. http://dx.doi.org/10.1093/oso/9780195122343.003.0024.

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Learning, like consciousness, is something that everybody can recognize and no one can define without provoking controversy. Perhaps this is why some important books dedicate hundreds of pages to learning without defining it (e.g., Mackintosh, 1974; Marler and Terrace, 1984). In one unusually candid book, the indexed page that promised a definition of learning proved to be completely blank. That stimulated me to make my own definition, something that is easier for a person who is not an expert in the field: learning is a change in the nervous system manifested as altered behavior due to experience (based on discussions in Marler and Terrace, 1984; Bell, 1991; Mackintosh, 1974, 1983; Papaj, 1994). Most people, including most biologists, probably underestimate the importance of learning in the biology of nonhuman animals. But there have been important exceptions, for example, in the writings of Baldwin (1902), Hinde (1959), Partridge (1983), Roper (1983a,b), Slater (1983,1986), Shettleworth (1984), Davey (1989), Wcislo (1989), Real (1993, 1994), Dyer (1994), Morse (1980), Marler (1998), and others (see Marler and Terrace, 1984). Some form of learning, whether habituation, associative learning (Pavlovian conditioning, in which a reward or punishment is associated with some cue such a color, odor, or sound), aversive learning, or trial and error learning (operant conditioning, in which a rewarded behavior is repeated or a punished one stopped), seems to occur in all animal groups where there is enough versatility in movement to allow it to be recognized. The venerable animal psychology text by Maier and Schneirla (1935 [1964]) gives many interesting examples from a time when researchers sought to demonstrate learning in a wide variety of organisms. They found it even in protists. In more recent research in areas such as foraging behavior and kin recognition (e.g., see Heinrich, 1979; Fletcher and Michener, 1987), learning has proven to be important but is a sidelight to research more concerned with optimization and adaptation. So learning itself has not always received the attention it deserves as a phenomenon of general evolutionary interest.
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Conference papers on the topic "Real-time Text Recognition"

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Xie, Dong, Arthur C. Depoian, Lorenzo E. Jaques, Colleen P. Bailey, and Parthasarathy Guturu. "Novel technique for broadcast footage overlay text recognition." In Real-Time Image Processing and Deep Learning 2021, edited by Nasser Kehtarnavaz and Matthias F. Carlsohn. SPIE, 2021. http://dx.doi.org/10.1117/12.2588177.

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Neumann, L., and J. Matas. "Real-time scene text localization and recognition." In 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2012. http://dx.doi.org/10.1109/cvpr.2012.6248097.

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Thomas, Philippe, Johannes Kirschnick, Leonhard Hennig, Renlong Ai, Sven Schmeier, Holmer Hemsen, Feiyu Xu, and Hans Uszkoreit. "Streaming Text Analytics for Real-Time Event Recognition." In RANLP 2017 - Recent Advances in Natural Language Processing Meet Deep Learning. Incoma Ltd. Shoumen, Bulgaria, 2017. http://dx.doi.org/10.26615/978-954-452-049-6_096.

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Gomez, Llifs, and Dimosthenis Karatzas. "MSER-Based Real-Time Text Detection and Tracking." In 2014 22nd International Conference on Pattern Recognition (ICPR). IEEE, 2014. http://dx.doi.org/10.1109/icpr.2014.536.

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Yang, Haojin, Cheng Wang, Xiaoyin Che, Sheng Luo, and Christoph Meinel. "An Improved System For Real-Time Scene Text Recognition." In ICMR '15: International Conference on Multimedia Retrieval. New York, NY, USA: ACM, 2015. http://dx.doi.org/10.1145/2671188.2749352.

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Liu, Yi, Dongming Zhang, Yongdong Zhang, and Shouxun Lin. "Real-Time Scene Text Detection Based on Stroke Model." In 2014 22nd International Conference on Pattern Recognition (ICPR). IEEE, 2014. http://dx.doi.org/10.1109/icpr.2014.537.

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Wu, Qingtian, Peng Chen, and Yimin Zhou. "A Scalable System to Synthesize Data for Natural Scene Text Localization and Recognition." In 2019 IEEE International Conference on Real-time Computing and Robotics (RCAR). IEEE, 2019. http://dx.doi.org/10.1109/rcar47638.2019.9043965.

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Li, Jiachen, Yuan Lin, Rongrong Liu, Chiu Man Ho, and Humphrey Shi. "RSCA: Real-time Segmentation-based Context-Aware Scene Text Detection." In 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). IEEE, 2021. http://dx.doi.org/10.1109/cvprw53098.2021.00267.

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9

Liu, Yuliang, Hao Chen, Chunhua Shen, Tong He, Lianwen Jin, and Liangwei Wang. "ABCNet: Real-Time Scene Text Spotting With Adaptive Bezier-Curve Network." In 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2020. http://dx.doi.org/10.1109/cvpr42600.2020.00983.

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Zhu, Ming, Huakang Li, Xiaoyu Sun, and Zhuo Yang. "BLAC: A Named Entity Recognition Model Incorporating Part-of-Speech Attention in Irregular Short Text." In 2020 IEEE International Conference on Real-time Computing and Robotics (RCAR). IEEE, 2020. http://dx.doi.org/10.1109/rcar49640.2020.9303256.

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