Relevant bibliographies by topics / Optical Character Identification

Contents

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

Academic literature on the topic 'Optical Character Identification'

Author: Grafiati
Published: 4 June 2025
Last updated: 14 July 2025

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Journal articles on the topic "Optical Character Identification"

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Lestari, Ikha Novie Tri, and Dadang Iskandar Mulyana. "Implementation of OCR (Optical Character Recognition) Using Tesseract in Detecting Character in Quotes Text Images." Journal of Applied Engineering and Technological Science (JAETS) 4, no. 1 (2022): 58–63. http://dx.doi.org/10.37385/jaets.v4i1.905.

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The development of technology in Indonesia is currently increasingly advanced in people's lives and cannot be avoided. The use of Artificial Intelligence in helping humans in dealing with problems is growing. Humans can take advantage of computer/smartphone media in today's technological era. One of its uses is Optical Character Recognition. This research is motivated by the problem where the running system requires development in terms of technology to detect characters in the quote text image, because the previous system still performs manual input. Optical Character Recognition has been wid
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Wu, Yifan, and Yuxi Zhang. "Optical character recognition with different languages." Applied and Computational Engineering 17, no. 1 (2023): 60–64. http://dx.doi.org/10.54254/2755-2721/17/20230914.

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Optical character recognition is the combination of optical technology and computer technology to identify text in an image and then recognize the text content in the image, providing individuals with a great deal of ease in their daily lives. Document text recognition, natural scene text recognition, bill text recognition, and ID card recognition have been used in daily life, but there are still many factors that lead to inaccurate identification and detection. Therefore, different texts, patterns or characters are suitable for different types of Optical character recognition. In this paper,
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Narendra, Sahu, and Sonkusare Manoj. "A STUDY ON OPTICAL CHARACTER RECOGNITION TECHNIQUES." International Journal of Computational Science, Information Technology and Control Engineering (IJCSITCE) 4, no. 1 (2017): 1–14. https://doi.org/10.5121/ijcsitce.2017.4101.

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Optical Character Recognition (OCR) is the process which enables a system to without human intervention identifies the scripts or alphabets written into the users’ verbal communication. Optical Character identification has grown to be individual of the mainly flourishing applications of knowledge in the field of pattern detection and artificial intelligence. In our survey we study on the various OCR techniques. In this paper we resolve and examine the hypothetical and numerical models of Optical Character Identification. The Optical character identification or classification (OCR) and Ma
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Gurav, Savitri. "Review of methods for Handwritten Character Identification using Optical Character Recognition (OCR)." International Journal for Research in Applied Science and Engineering Technology 7, no. 6 (2019): 2508–11. http://dx.doi.org/10.22214/ijraset.2019.6422.

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Oudah, Nabeel, Maher Faik Esmaile, and Estabraq Abdulredaa. "Optical Character Recognition Using Active Contour Segmentation." Journal of Engineering 24, no. 1 (2018): 146–58. http://dx.doi.org/10.31026/j.eng.2018.01.10.

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Document analysis of images snapped by camera is a growing challenge. These photos are often poor-quality compound images, composed of various objects and text; this makes automatic analysis complicated. OCR is one of the image processing techniques which is used to perform automatic identification of texts. Existing image processing techniques need to manage many parameters in order to clearly recognize the text in such pictures. Segmentation is regarded one of these essential parameters. This paper discusses the accuracy of segmentation process and its effect over the recognition process. Ac
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Shanmugavel, Subramanian, Jagadeesh Kannan, Arjun Vaithilingam Sudhakar, and . "Handwritten Optical Character Extraction and Recognition from Catalogue Sheets." International Journal of Engineering & Technology 7, no. 4.5 (2018): 36. http://dx.doi.org/10.14419/ijet.v7i4.5.20005.

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The dataset consists of 20000 scanned catalogues of fossils and other artifacts compiled by the Geological Sciences Department. The images look like a scanned form filled with blue ink ball pen. The character extraction and identification is the first phase of the research and in the second phase we are planning to use the HMM model to extract the entire text from the form and store it in a digitized format. We used various image processing and computer vision techniques to extract characters from the 20000 handwritten catalogues. Techniques used for character extraction are Erode, MorphologyE
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Hyun, Young-Joo, Eunseok Nam, and Youngjun Yoo. "Real-time Optical Character Recognition in Manufacturing Using YOLOv8 and Embedded Systems for Engraved Characters on a Metal Surface." International Journal of Precision Engineering and Manufacturing-Smart Technology 3, no. 2 (2025): 107–15. https://doi.org/10.57062/ijpem-st.2025.00052.

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This study introduces a YOLOv8-based Optical Character Recognition (OCR) system specifically optimized for engraved character recognition, aiming to facilitate digital transformation and enhance smart manufacturing processes. To overcome limitations of manual part identification and quality inspection prevalent in conventional manufacturing environments, this study employed engraved character data from metal scroll compressor components. A lightweight deep learning model was designed and deployed on a Raspberry Pi platform to enable real-time character recognition. In a controlled laboratory e
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Muthusundari, Muthusundari, A. Velpoorani, S. Venkata Kusuma, Trisha L, and Om k. Rohini. "Optical character recognition system using artificial intelligence." LatIA 2 (August 13, 2024): 98. http://dx.doi.org/10.62486/latia202498.

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Abstract A technique termed optical character recognition, or OCR, is used to extract text from images. An OCR the system's primary goal is to transform already present paper-based paperwork or picture data into usable papers. Character as well as word detection are the two main phases of an OCR, which is designed using many algorithms. An OCR also maintains a document's structure by focusing on sentence identification, which is a more sophisticated approach. Research has demonstrated that despite the efforts of numerous scholars, no error-free Bengali OCR has been produced. This issue is addr
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Veni, S., R. S. Sabeenian, T. Shanthi, and R. Anand. "Real time noisy dataset implementation of optical character identification using CNN." International Journal of Intelligent Enterprise 7, no. 1/3 (2020): 67. http://dx.doi.org/10.1504/ijie.2020.10026346.

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Anand, R., T. Shanthi, R. S. Sabeenian, and S. Veni. "Real time noisy dataset implementation of optical character identification using CNN." International Journal of Intelligent Enterprise 7, no. 1/2/3 (2020): 67. http://dx.doi.org/10.1504/ijie.2020.104646.

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Dissertations / Theses on the topic "Optical Character Identification"

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Bayless, Mark D. "Improving optical character recognition accuracy for cargo container identification numbers." [Denver, Colo.] : Regis University, 2010. http://adr.coalliance.org/codr/fez/view/codr:139.

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Lau, Kai Kwong Gervas. "A new statistical stroke recovery method and measurement for signature verification." HKBU Institutional Repository, 2005. http://repository.hkbu.edu.hk/etd_ra/661.

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Pang, Bo. "Handwriting Chinese character recognition based on quantum particle swarm optimization support vector machine." Thesis, University of Macau, 2018. http://umaclib3.umac.mo/record=b3950620.

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Dvořáková, Alena. "Studie řízení plynulých materiálových toků s využitím značení produktů." Master's thesis, Vysoké učení technické v Brně. Fakulta podnikatelská, 2008. http://www.nusl.cz/ntk/nusl-221599.

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Master´s thesis analyses current methods and procedures of storing and marking of goods of Disk obchod & technika, spol. s.r.o. company. It includes the proposal of goods identification which leads to the optimizing of continuous flows from the point of view of both simplification and acceleration of work and simpler and more accurate ways of goods identification. The proposal is related to the choice of appropriate method of goods identification and the selection of particular type of barcodes, including the necessary hardware.
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Tsai, Wen-Chuan, and 蔡文川. "Identification of the optical and mechanical character in GLV devices." Thesis, 2004. http://ndltd.ncl.edu.tw/handle/73982954170413297541.

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碩士<br>國立臺北科技大學<br>光電技術研究所<br>92<br>The GLV chip may apply in the optical fiber communication VOA accent changes and in the laser projection monitor several chips part, it has the reaction rate quick, the high contrast gradient, color high, the volume small and the quality is light and so on the superiority; The GLV laser projected display system, it by its high quality image and the high-purity fresh color, may become the amazing new generation of display system. Present GLV under the SLM development, already developed to several- electronic chips third generation, how but were gene
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Ghosh, Debashis. "A Possibilistic Approach To Handwritten Script Identification Via Morphological Methods For Pattern Representation." Thesis, 1999. https://etd.iisc.ac.in/handle/2005/1673.

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Ghosh, Debashis. "A Possibilistic Approach To Handwritten Script Identification Via Morphological Methods For Pattern Representation." Thesis, 1999. http://etd.iisc.ernet.in/handle/2005/1673.

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Books on the topic "Optical Character Identification"

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LaMoreaux, Robert D. Barcodes and other automatic identification systems. Pira International, 1995.

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Sabourin, Conrad. Optical character recognition and document segmentation: Character preprocessing, thinning, isolation, segmentation, feature extraction, cursive and multi-font recognition, writer/scriptor identification : bibliography. Infolingua, 1994.

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Adams, Russell E. Sourcebook of automatic identification and data collection. Van Nostrand Reinhold, 1990.

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Adams, Russ. Sourcebook of automatic identification and data collection. Van Nostrand Reinhold, 1990.

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Gorskiĭ, N. D. Razpoznavanie rukopisnogo teksta: Ot teorii k praktike. Politekhnika, 1997.

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Philip, Lopresti Daniel, Zhou Jiangying, IS & T--the Society for Imaging Science and Technology., and Society of Photo-optical Instrumentation Engineers., eds. Document recognition and retrieval VI: 27-28 January 1999, San Jose, California. SPIE, 1999.

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Barney, Smith Elisa H., Taghva Kazem, IS & T--the Society for Imaging Science and Technology., and Society of Photo-optical Instrumentation Engineers., eds. Document recognition and retrieval XII: 19-20 January 2005, San Jose, California, USA. SPIE, 2005.

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Philip, Lopresti Daniel, Zhou Jiangying, IS & T--the Society for Imaging Science and Technology., and Society of Photo-optical Instrumentation Engineers., eds. Document recognition and retrieval VII: 26-27 January, 2000, San Jose, California. SPIE, 2000.

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name, No. Document recognition and retrieval X: 22-24 January 2003, Santa Clara, California, USA. SPIE, 2002.

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A, Fink Gernot, and SpringerLink (Online service), eds. Markov Models for Handwriting Recognition. Thomas Plötz, 2011.

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Book chapters on the topic "Optical Character Identification"

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Schlüter, Marian, Christian Tepper, Clemens Briese, Ole Kroeger, Raul Vicente-Garcia, and Jörg Krüger. "Deep Learning-Based Optical Character Recognition for Identifying On-Label Printed Part Numbers of Used Automotive Parts: A Comparative Study of Open Source and Commercial Methods." In Lecture Notes in Mechanical Engineering. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-77429-4_58.

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AbstractThis paper explores the use of deep learning-based optical character recognition (OCR) to identify part numbers for used automotive parts. It compares open source and advanced AI methods to commercial tools from Google, Amazon, and Microsoft. The study finds that fine-tuned open source models outperform commercial services, especially for complex part numbers unrelated to any language structure. The preferred open source method, MaskedTextSpotter, is fine-tuned with image data from old vehicle and electrical parts, captured by a smartphone and 2D barcode scanner. Additionally, a new data augmentation method, CharChan, is introduced, replacing detected characters with random examples for better character recognition. The experiments demonstrate the efficacy of deep learning-based OCR for automotive part number identification.
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Ye, Wei-min, and David J. Hunt. "Measuring nematodes and preparation of figures." In Techniques for work with plant and soil nematodes. CABI, 2021. http://dx.doi.org/10.1079/9781786391759.0132.

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Abstract This chapter focuses on the importance of accurate measurements for the description and identification of nematodes. Differences, albeit small yet consistent, can, if accurately recorded, be important for distinguishing taxa at the species level, although the usefulness and reliability of these characters may only be applicable to certain taxa. Measurement errors resulting from the calibration of the optical system, operator accuracy or even by the same operator measuring the same specimen but on different occasions, are discussed. The effects of the way in which nematodes are prepared for study in temporary water mounts and the killing, fixing and processing methods employed, as does the way in which the slide mount is made, on the morphometric characters of nematodes are also pointed out.
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Ye, Wei-min, and David J. Hunt. "Measuring nematodes and preparation of figures." In Techniques for work with plant and soil nematodes. CABI, 2021. http://dx.doi.org/10.1079/9781786391759.0007.

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Abstract This chapter focuses on the importance of accurate measurements for the description and identification of nematodes. Differences, albeit small yet consistent, can, if accurately recorded, be important for distinguishing taxa at the species level, although the usefulness and reliability of these characters may only be applicable to certain taxa. Measurement errors resulting from the calibration of the optical system, operator accuracy or even by the same operator measuring the same specimen but on different occasions, are discussed. The effects of the way in which nematodes are prepared for study in temporary water mounts and the killing, fixing and processing methods employed, as does the way in which the slide mount is made, on the morphometric characters of nematodes are also pointed out.
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Sarwar, Hasan, Mizanur Rahman, Nasreen Akter, Saima Hossain, Sabrina Ahmed, and Chowdhury Mofizur Rahman. "Selection of an Optimal Set of Features for Bengali Character Recognition." In Technical Challenges and Design Issues in Bangla Language Processing. IGI Global, 2013. http://dx.doi.org/10.4018/978-1-4666-3970-6.ch005.

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Feature extraction is an essential step of Optical Character Recognition. Accurate and distinguishable feature plays a significant role to leverage the performance of a classifier. The complexity level of feature identification algorithm differs for alphabet sets of different languages. Apart from generic algorithms to find features of different alphabet sets, these algorithms take care of individual characteristic common for a particular alphabet set. Dominant features of one alphabet set might completely differ from that of another set. Since there always remains the chance that inaccurate features may cause inefficient recognition, special attention should be given to identify the set of optimal features of a character set. Bengali characters also have some specific issues apart from the existing issues of other character sets. For example, there are about 300 basic, modified, and compound character shapes in the script, the characters in a word are topologically connected, and Bengali is an inflectional language. Literature survey shows that several authors have used different features and classification algorithms. The authors have extensively reviewed all these feature sets. In order to identify an optimal feature set, variability analysis has been proposed here. They focus on the specific peculiarities of Bengali alphabet sets, its different usage as vowel and consonant signs, compound, complex, and touching characters. The authors also took care to generate easily computable features that take less time for generation. However, more attention needs to be given in order to choose an efficient classifier.
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"A novel technique for script identification in trilingual optical character recognition." In Emerging Trends in Engineering, Science and Technology for Society, Energy and Environment. CRC Press, 2018. http://dx.doi.org/10.1201/9781351124140-144.

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Parvathi, R., Savio Sajan Moloparambil, Aswathi M. Kumar, and R. Jeyahari. "Automated Vehicle Number Plate Detection Using Tesseract and Paddleocr." In Recent Developments in Machine and Human Intelligence. IGI Global, 2023. http://dx.doi.org/10.4018/978-1-6684-9189-8.ch007.

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Automatic number plate recognition (ANPR) is a specialized image processing method that identifies the text on a given vehicle's number plate. The goal is to create a successful automatic approved vehicle identification system that makes use of the license plate. The system may be placed in many scenarios and locations, some of which may include security in prohibited areas like military and testing zones, or the vicinity of important government buildings like the Supreme Court, Parliament, etc. Using image segmentation in an image, the region containing the vehicle number plate from the image of a vehicle is extracted. Character recognition is achieved using an optical character recognition (OCR) approach in order to determine miscellaneous details like the owner of any detected vehicle, the location of registration, the address and whereabouts, etc.
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Karaiskos, Dimitrios C., and Panayiotis E. Kourouthanassis. "Determinants of User Acceptance for RFID Ticketing Systems." In Ubiquitous and Pervasive Computing. IGI Global, 2010. http://dx.doi.org/10.4018/978-1-60566-960-1.ch069.

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The term RFID refers to radio frequency identification and describes transponders or tags that are attached to animate or inanimate objects and are automatically read by a network infrastructure or networked reading devices. Current solutions such as optical character recognition (OCR), bar codes, or smart card systems require manual data entry, scanning, or readout along the supply chain. These procedures are costly, time consuming, and inaccurate. RFID systems are seen as a potential solution to these constraints, by allowing non-line-of-sight reception of the coded data. Identification codes are stored on a tag that consists of a microchip and an attached antenna. Once the tag is within the reception area of a reader, the information is transmitted. A connected database is then able to decode the identification code and identify the object. Such network infrastructures should be able to capture, store, and deliver large amounts of data robustly and efficiently (Scharfeld, 2001).
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Raju, Anand, and Shanthi Thirunavukkarasu. "Convolutional Neural Network Demystified for a Comprehensive Learning with Industrial Application." In Dynamic Data Assimilation - Beating the Uncertainties. IntechOpen, 2020. http://dx.doi.org/10.5772/intechopen.92091.

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In the recent past of time, numerous investigators have driven on and subsidized novelties to image classification methods. In this chapter, an introduction to image classification scheme and their types is offered. Image classification discovers its application in a variety of fields, to name a few, judgment of diseases, finding and identification of faults, classification of nutrition goods based on superiority, valuation of usual capitals and conservation pollution, education of land use and land cover from remote sensing satellite images, character identification and detection in optical character reader, face recognition, object detection, and so on. Automatic image classification schemes found on actual algorithms deliver high accuracy and exactness in recognizing object/features. Convolution neural network is a superior genre of neural network that requires minimal preprocessing. The ability of the convolutional neural network (CNN) to understand the visual content of the input image makes its suitable for recognizing minute variation between the classes. This power of the CNN makes it a good choice to address image classification problems with multi-classes. So, in this chapter, the entire flow of CNN’s architecture with different industrial applications will be discussed.
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Grouin Cyril and Zweigenbaum Pierre. "Automatic De-Identification of French Clinical Records: Comparison of Rule-Based and Machine-Learning Approaches." In Studies in Health Technology and Informatics. IOS Press, 2013. https://doi.org/10.3233/978-1-61499-289-9-476.

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In this paper, we present a comparison of two approaches to automatically de-identify medical records written in French: a rule-based system and a machine-learning based system using a conditional random fields (CRF) formalism. Both systems have been designed to process nine identifiers in a corpus of medical records in cardiology. We performed two evaluations: first, on 62 documents in cardiology, and on 10 documents in foetopathology &amp;ndash; produced by optical character recognition (OCR) &amp;ndash; to evaluate the robustness of our systems. We achieved a 0.843 (rule-based) and 0.883 (machine-learning) exact match overall F-measure in cardiology. While the rule-based system allowed us to achieve good results on nominative (first and last names) and numerical data (dates, phone numbers, and zip codes), the machine-learning approach performed best on more complex categories (postal addresses, hospital names, medical devices, and towns). On the foetopathology corpus, although our systems have not been designed for this corpus and despite OCR character recognition errors, we obtained promising results: a 0.681 (rule-based) and 0.638 (machine-learning) exact-match overall F-measure. This demonstrates that existing tools can be applied to process new documents of lower quality.
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Amara, Marwa, and Kamel Zidi. "New Mechanisms to Enhance the Performances of Arabic Text Recognition System." In Handbook of Research on Machine Learning Innovations and Trends. IGI Global, 2017. http://dx.doi.org/10.4018/978-1-5225-2229-4.ch038.

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The recognition of a character begins with analyzing its form and extracting the features that will be exploited for the identification. Primitives can be described as a tool to distinguish an object of one class from another object of another class. It is necessary to define the significant primitives during the development of an optical character recognition system. Primitives are defined by experience or by intuition. Several primitives can be extracted while some are irrelevant or redundant. The size of vector primitives can be large if a large number of primitives are extracted including redundant and irrelevant features. As a result, the performance of the recognition system becomes poor, and as the number of features increases, so does the computing time. Feature selection, therefore, is required to ensure the selection of a subset of features that gives accurate recognition and has low computational overhead. We use feature selection techniques to improve the discrimination capacity of the Multilayer Perceptron Neural Networks (MLPNNs).
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Conference papers on the topic "Optical Character Identification"

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Marne, Mrunal G., Pravin R. Futane, Sakshi B. Kolekar, Aditya D. Lakhadive, and Snehwardhan K. Marathe. "Identification of Optimal Optical Character Recognition (OCR) Engine for Proposed System." In 2018 Fourth International Conference on Computing Communication Control and Automation (ICCUBEA). IEEE, 2018. http://dx.doi.org/10.1109/iccubea.2018.8697487.

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Kartiwi, Mira, Teddy Surya Gunawan, Aqmarina Anwar, and Siti Sarah Fathurohmah. "Mobile Application for Halal Food Ingredients Identification using Optical Character Recognition." In 2018 IEEE 5th International Conference on Smart Instrumentation, Measurement and Application (ICSIMA). IEEE, 2018. http://dx.doi.org/10.1109/icsima.2018.8688756.

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Qadri, Muhammad Tahir, and Muhammad Asif. "Automatic Number Plate Recognition System for Vehicle Identification Using Optical Character Recognition." In 2009 International Conference on Education Technology and Computer. IEEE, 2009. http://dx.doi.org/10.1109/icetc.2009.54.

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Himes, Glenn S., Marty M. Scholl, and Frank A. DeCosta III. "Identification and correction of rejection and substitution errors in optical character recognition systems." In IS&T/SPIE's Symposium on Electronic Imaging: Science and Technology, edited by Donald P. D'Amato. SPIE, 1993. http://dx.doi.org/10.1117/12.143616.

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Maitrichit, Nagorn, and Narit Hnoohom. "Intelligent Medicine Identification System Using a Combination of Image Recognition and Optical Character Recognition." In 2020 15th International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP). IEEE, 2020. http://dx.doi.org/10.1109/isai-nlp51646.2020.9376816.

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Achmadi, Fuad, Fathur Rachman Nufaily, Afdi Fauzul Bahar, and Shofwatul Uyun. "Application of Optical Character Recognizer Prototype and Convulsion Neural Network for Vehicle License Plate Detection." In The 6th International Conference on Science and Engineering. Trans Tech Publications Ltd, 2024. http://dx.doi.org/10.4028/p-2fj9dn.

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License plates play an important role in vehicle identification in a variety of applications such as traffic safety, parking management, and traffic enforcement. In this study, we propose the development of license plate recognition applications using optical character recognition (OCR) and convolutional neural network (CNN) techniques. The OCR method is used to recognize characters on license plates and the CNN method is used to recognize license plates in images. The purpose of this research is to develop a system that can automatically recognize and recognize license plates in images. The O
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Ostojić, Gordana, and Stevan Stankovski. "AUTOMATIC IDENTIFICATION SYSTEMS IN INDUSTRY APPLICATIONS." In INTERNATIONAL Conference on Business, Management, and Economics Engineering Future-BME. Faculty of Technical Sciences, Novi Sad, 2025. https://doi.org/10.24867/future-bme-2024-121.

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Automatic Identification Systems have become a cornerstone in enhancing operational efficiency and security across various industrial sectors. This paper provides a comprehensive exploration of these technologies, including Radio Frequency Identification (RFID), barcode systems, Optical Character Recognition (OCR), and Internet of Things (IoT)-based solutions. We investigate their applications in diverse industries such as manufacturing, logistics, healthcare, and retail, highlighting the transformative impact on supply chain management, asset tracking, inventory control, and personnel authent
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Bratić, Diana, and Nikolina Stanić Loknar. "AI driven OCR: Resolving handwritten fonts recognizability problems." In 10th International Symposium on Graphic Engineering and Design. University of Novi Sad, Faculty of technical sciences, Department of graphic engineering and design,, 2020. http://dx.doi.org/10.24867/grid-2020-p82.

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Optical Character Recognition (OCR) is the electronic or mechanical conversion of images of typed, handwritten, or printed text into machine-encoded text. Advanced systems are capable to produce a high degree of recognition accuracy for most technic fonts, but when it comes to handwritten forms there is a problem occur in recognizing certain characters and limitations with conventional OCR processes persist. It is most pronounced in ascenders (k, b, l, d, h, t) and descenders (g, j, p, q, y). If the characters are linked by ligatures, the ascending and descending strokes are even less recogniz
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Soichi, Sugiyama, and Noriyuki Kida. "Individual characteristics using pen writing behavior: intra- and inter-individual variability perspectives." In 16th International Conference on Applied Human Factors and Ergonomics (AHFE 2025). AHFE International, 2025. https://doi.org/10.54941/ahfe1006639.

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Several personal authentication technologies are currently available. Writing movements are consistent among individuals, and each person has unique writing habits. Therefore, this study aims to evaluate the intra and inter-individual variability in pen angles to determine whether writing motions can be used for personal authentication. Sixteen right-handed adults participated in this study. Each participant was asked to write a Japanese name consisting of four kanji characters while seated on a chair. This task was repeated five times. Three-dimensional coordinate data were recorded from both
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Apostol, Silviu, Loredana Zaharescu, and Isabelle Alexe. "GAMIFICATION OF LEARNING AND EDUCATIONAL GAMES." In eLSE 2013. Carol I National Defence University Publishing House, 2013. http://dx.doi.org/10.12753/2066-026x-13-118.

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Studies have shown that game elements such as rewards and progress bars are generally associated with extrinsic motivation [Malone and Lepper, 1987], which may decrease the level of intrinsic motivation [Deci et al. 2001]. Extrinsic motivation may be useful for games aiming at acquiring declarative knowledge. These games usually involve labeling, drilling, matching or drag and drop. As the activity implies repetitive, routine actions, keeping score and offering rewards is an important part of keeping the player motivated to go through with the game [Kapp, 2012]. Extrinsic motivation seems to b
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