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Journal articles on the topic 'Optical music recognition (OMR)'

Author: Grafiati

Published: 7 June 2025

Last updated: 16 July 2025

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1

Purwito, Kevin. "Pengantar dan Survey Tentang Optical Music Recognition." Jurnal ULTIMATICS 6, no. 1 (2014): 36–39. http://dx.doi.org/10.31937/ti.v6i1.331.

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This paper describes about one of the many extension of Optical Character Recognition (OCR), that is Optical Music Recognition (OMR). OMR is used to recognize musical sheets into digital format, such as MIDI or MusicXML. There are many musical symbols that usually used in musical sheets and therefore needs to be recognized by OMR, such as staff; treble, bass, alto and tenor clef; sharp, flat and natural; beams, staccato, staccatissimo, dynamic, tenuto, marcato, stopped note, harmonic and fermata; notes; rests; ties and slurs; and also mordent and turn. OMR usually has four main processes, name

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Chen, Gen Fang, and Wen Jun Zhang. "An Overview of Optical Music Recognition in China." Advanced Materials Research 225-226 (April 2011): 223–27. http://dx.doi.org/10.4028/www.scientific.net/amr.225-226.223.

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OMR (Optical Music Recognition) is a technology for digital musical score image processing and recognition by computer, which has broad applications in the digital music library, contemporary music education, music theory, music automatic classification, music and audio sync dissemination and etc. This paper first has a brief description of OMR research and focuses on describing the research of Chinese OMR literature, it represents the research status and results in China, then the paper pointes out that the target of OMR research in China must tend to Chinese traditional musical score image p

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Diet, Jürgen. "Optical Music Recognition in der Bayerischen Staatsbibliothek." Bibliothek Forschung und Praxis 42, no. 2 (2018): 319–23. http://dx.doi.org/10.1515/bfp-2018-0030.

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Zusammenfassung Die Bayerische Staatsbibliothek hat im Sommer 2016 ein Projekt zur automatischen Notenerkennung (Optical Music Recognition, OMR) gestartet. Dieser Beitrag beschreibt zunächst die Funktionsweise und die Qualität von OMR-Programmen und geht dann auf die bisherigen Erfahrungen der Bayerischen Staatsbibliothek mit OMR ein. Anschließend werden die Anwendungsszenarien skizziert, die die Bayerische Staatsbibliothek aufsetzend auf den OMR-Daten entwickeln wird.1

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Xiao, Zhe, Xin Chen, and Li Zhou. "Real-Time Optical Music Recognition System for Dulcimer Musical Robot." Journal of Advanced Computational Intelligence and Intelligent Informatics 23, no. 4 (2019): 782–90. http://dx.doi.org/10.20965/jaciii.2019.p0782.

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Traditional optical music recognition (OMR) is an important technology that automatically recognizes scanned paper music sheets. In this study, traditional OMR is combined with robotics, and a real-time OMR system for a dulcimer musical robot is proposed. This system gives the musical robot a stronger ability to perceive and understand music. The proposed OMR system can read music scores, and the recognized information is converted into a standard electronic music file for the dulcimer musical robot, thus achieving real-time performance. During the recognition steps, we treat note groups and i

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Ferano, Francisco Calvin Arnel, Amalia Zahra, and Gede Putra Kusuma. "Stacking ensemble learning for optical music recognition." Bulletin of Electrical Engineering and Informatics 12, no. 5 (2023): 3095–104. http://dx.doi.org/10.11591/eei.v12i5.5129.

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The development of music culture has resulted in a problem called optical music recognition (OMR). OMR is a task in computer vision that explores the algorithms and models to recognize musical notation. This study proposed the stacking ensemble learning model to complete the OMR task using the common western musical notation (CWMN) musical notation. The ensemble learning model used four deep convolutional neural networks (DCNNs) models, namely ResNeXt50, Inception-V3, RegNetY-400MF, and EfficientNet-V2-S as the base classifier. This study also analysed the most appropriate technique to be used

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CHEN, YUNG-SHENG, FENG-SHENG CHEN, and CHIN-HUNG TENG. "AN OPTICAL MUSIC RECOGNITION SYSTEM FOR SKEW OR INVERTED MUSICAL SCORES." International Journal of Pattern Recognition and Artificial Intelligence 27, no. 07 (2013): 1353005. http://dx.doi.org/10.1142/s0218001413530054.

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Optical Music Recognition (OMR) is a technique for converting printed musical documents into computer readable formats. In this paper, we present a simple OMR system that can perform well for ordinary musical documents such as ballad and pop music. This system is constructed based on fundamental image processing and pattern recognition techniques, thus it is easy to implement. Moreover, this system has a strong capability in skew restoration and inverted musical score detection. From a series of experiments, the error for our skew restoration is below 0.2° for any possible document rotation an

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Hartelt, Alexander, Tim Eipert, and Frank Puppe. "Optical Medieval Music Recognition—A Complete Pipeline for Historic Chants." Applied Sciences 14, no. 16 (2024): 7355. http://dx.doi.org/10.3390/app14167355.

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Manual transcription of music is a tedious work, which can be greatly facilitated by optical music recognition (OMR) software. However, OMR software is error prone in particular for older handwritten documents. This paper introduces and evaluates a pipeline that automates the entire OMR workflow in the context of the Corpus Monodicum project, enabling the transcription of historical chants. In addition to typical OMR tasks such as staff line detection, layout detection, and symbol recognition, the rarely addressed tasks of text and syllable recognition and assignment of syllables to symbols ar

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Wu, Fu-Hai Frank. "Applying Machine Learning in Optical Music Recognition of Numbered Music Notation." International Journal of Multimedia Data Engineering and Management 8, no. 3 (2017): 21–41. http://dx.doi.org/10.4018/ijmdem.2017070102.

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Although research of optical music recognition (OMR) has existed for few decades, most of efforts were put in step of image processing to approach upmost accuracy and evaluations were not in common ground. And major music notations explored were the conventional western music notations with staff. On contrary, the authors explore the challenges of numbered music notation, which is popular in Asia and used in daily life for sight reading. The authors use different way to improve recognition accuracy by applying elementary image processing with rough tuning and supplementing with methods of mach

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Huang, Zhiqing, Xiang Jia, and Yifan Guo. "State-of-the-Art Model for Music Object Recognition with Deep Learning." Applied Sciences 9, no. 13 (2019): 2645. http://dx.doi.org/10.3390/app9132645.

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Optical music recognition (OMR) is an area in music information retrieval. Music object detection is a key part of the OMR pipeline. Notes are used to record pitch and duration and have semantic information. Therefore, note recognition is the core and key aspect of music score recognition. This paper proposes an end-to-end detection model based on a deep convolutional neural network and feature fusion. This model is able to directly process the entire image and then output the symbol categories and the pitch and duration of notes. We show a state-of-the-art recognition model for general music

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Yang, Yin Xian, Li Zhao, Cai Rong Zou, and Yin Xian Yang. "Staff Line Removal Algorithm and Research Based on Run-Length Graph Slice and Topological Structure of Music." Advanced Materials Research 760-762 (September 2013): 1429–33. http://dx.doi.org/10.4028/www.scientific.net/amr.760-762.1429.

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Staff line removal is a key step before segmentation and recognition of music image and plays an important role in OMR (Optical Music Recognition) research, the result of staff line removal directly influences the performance and function of the whole OMR system. However, over-removal and under-removal often occurs in the processing and leads to the low efficiency of music recognition rate. So, in order to solve the arduous problem, an approach based on run-length graph slice and topological structure of music is put forward by careful analysis of staff line and music notation structure. Exper

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Hartelt, Alexander, and Frank Puppe. "Optical Medieval Music Recognition Using Background Knowledge." Algorithms 15, no. 7 (2022): 221. http://dx.doi.org/10.3390/a15070221.

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This paper deals with the effect of exploiting background knowledge for improving an OMR (Optical Music Recognition) deep learning pipeline for transcribing medieval, monophonic, handwritten music from the 12th–14th century, whose usage has been neglected in the literature. Various types of background knowledge about overlapping notes and text, clefs, graphical connections (neumes) and their implications on the position in staff of the notes were used and evaluated. Moreover, the effect of different encoder/decoder architectures and of different datasets for training a mixed model and for docu

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Liu, Yipeng, Ruimin Wu, Yifan Wu, Lijie Luo, and Wei Xu. "A Stave-Aware Optical Music Recognition on Monophonic Scores for Camera-Based Scenarios." Applied Sciences 13, no. 16 (2023): 9360. http://dx.doi.org/10.3390/app13169360.

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The recognition of printed music sheets in camera-based realistic scenarios is a novel research branch of optical music recognition (OMR). However, special factors in realistic scenarios, such as uneven lighting distribution and curvature of staff lines, can have adverse effects on OMR models designed for digital music scores. This paper proposes a stave-aware method based on object detection to recognize monophonic printed sheet music in camera-based scenarios. By detecting the positions of staff lines, we improve the accuracy of note pitch effectively. In addition, we present the Camera Prin

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Ru, Yingdong. "Computer Assisted Chord Detection Using Deep Learning and YOLOV4 Neural Network Model." Journal of Physics: Conference Series 2083, no. 4 (2021): 042017. http://dx.doi.org/10.1088/1742-6596/2083/4/042017.

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Abstract Music symbol recognition is an important part of Optical Music Recognition (OMR), Chord recognition is one of the most important research contents in the field of music information retrieval. It plays an important role in information processing, music structure analysis, and recommendation systems. Aiming at the problem of low chord recognition accuracy in the OMR recognition model, the article proposes a chord recognition method based on the YOLOV4 neural network model. First, the YOLOV4 network model is used to train single-voice scores to obtain the best training model. Then, the s

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Abdulazeez, Rana L., and Fattah Alizadeh. "Deep Learning-Based Optical Music Recognition for Semantic Representation of Non-overlap and Overlap Music Notes." ARO-THE SCIENTIFIC JOURNAL OF KOYA UNIVERSITY 12, no. 1 (2024): 79–87. http://dx.doi.org/10.14500/aro.11402.

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In the technology era, the process of teaching a computer to interpret musical notation is termed optical music recognition (OMR). It aims to convert musical note sheets presented in an image into a computer-readable format. Recently, the sequence-to-sequence model along with the attention mechanism (which is used in text and handwritten recognition) has been used in music notes recognition. However, due to the gradual disappearance of excessively long sequences of musical sheets, the mentioned OMR models which consist of long short-term memory are facing difficulties in learning the relations

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Wu, Fu Hai Frank, and Jyh Shing Roger Jang. "Optical Music Recognition for Numbered Music Notation with Multimodal Reconstruction." Applied Mechanics and Materials 479-480 (December 2013): 943–47. http://dx.doi.org/10.4028/www.scientific.net/amm.479-480.943.

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Optical music recognition (OMR) is attracted a lot attention on different music notation system which could be so focused on Back’s C-Clefs; in contrast, it could handle complete modern music symbols. One of notation system, numbered music notation, which is literally call “simplified notation”, is popular in many Asia countries. There is a traditional Chinese hymnbook, which usually used in small group of worship, in which one page has several hymns. We propose algorithms for the recognition of those notations in camera images of the hymn, which could effectively identify score zone and lyric

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Alfaro-Contreras, María, and Jose J. Valero-Mas. "Exploiting the Two-Dimensional Nature of Agnostic Music Notation for Neural Optical Music Recognition." Applied Sciences 11, no. 8 (2021): 3621. http://dx.doi.org/10.3390/app11083621.

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State-of-the-art Optical Music Recognition (OMR) techniques follow an end-to-end or holistic approach, i.e., a sole stage for completely processing a single-staff section image and for retrieving the symbols that appear therein. Such recognition systems are characterized by not requiring an exact alignment between each staff and their corresponding labels, hence facilitating the creation and retrieval of labeled corpora. Most commonly, these approaches consider an agnostic music representation, which characterizes music symbols by their shape and height (vertical position in the staff). Howeve

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Romão, Gustavo Henrique, Hygor Santiago Lara, and Jorge Nei Brito. "Testing YOLOv8’s efficacy as a pitch and duration detector across digitally written monophonic music scores." OBSERVATÓRIO DE LA ECONOMÍA LATINOAMERICANA 22, no. 9 (2024): e6776. http://dx.doi.org/10.55905/oelv22n9-133.

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Music presents itself as an important cultural landmark throughout human history that is largely recorded in physical sheets of paper that are prone to being degraded over time, resulting in irreparable loss. Optical Music Recognition (OMR) is the field within computer vision that has aimed, for decades, to try to mitigate this issue by finding means of which the music contained within music scores can be preserved in a machine-readable, replicable format. Even so, OMR has presented itself as a field of study with a considerable barrier of entry, not only because of the need for knowledge with

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Zhu, Juntong. "Comparative Analysis of Object Detection Models for Sheet Music Recognition: A Focus on YOLO and OMR Technologies." Applied and Computational Engineering 94, no. 1 (2024): 33–39. http://dx.doi.org/10.54254/2755-2721/94/2024melb0056.

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Abstract. As Artificial Intelligence (AI) technologies are developing rapidly and are widely used in various domains, it is efficient and convenient for composers to make music using AI to convert sheet music to audio. This research aims to compare the performance of different models in identifying individual notes within sheet music. Compared to traditional technologies like Optical Music Recognition (OMR), deep learning models have a significant advantage in processing blurry images with high efficiency. In the research process, three different models are used in searching for musical notes:

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Setyo, Ciara, and Gede Putra Kusuma. "Recognition of music symbol notation using convolutional neural network." International Journal of Electrical and Computer Engineering (IJECE) 14, no. 2 (2024): 2055. http://dx.doi.org/10.11591/ijece.v14i2.pp2055-2067.

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Musical notation is one thing that needs to be learned to play music. This notation has an important role in music because it can help in visualizing instructions for playing musical instruments and singing. Unfortunately, musical symbols that are commonly written in musical notation are difficult for beginners who have just started learning music. This research proposed a solution to create an optical music recognition (OMR) using a deep learning model to classify musical notes more accurately with some of the latest convolutional neural network (CNN) architectures. The research was carried o

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Setyo, Ciara, and Gede Putra Kusuma. "Recognition of music symbol notation using convolutional neural network." International Journal of Electrical and Computer Engineering (IJECE) 14, no. 2 (2024): 2055–67. https://doi.org/10.11591/ijece.v14i2.pp2055-2067.

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Musical notation is one thing that needs to be learned to play music. This notation has an important role in music because it can help in visualizing instructions for playing musical instruments and singing. Unfortunately, musical symbols that are commonly written in musical notation are difficult for beginners who have just started learning music. This research proposed a solution to create an optical music recognition (OMR) using a deep learning model to classify musical notes more accurately with some of the latest convolutional neural network (CNN) architectures. The research was carried o

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Nowitzki, Olaf, Corinna Engelhardt-Nowitzki, Martin L. Fiala, and Wilfried Wöber. "Optical Music Recognition of Printed White Mensural Notation: Conversion to Modern Notation Using Object Detection Mechanisms." International Journal of Humanities and Arts Computing 16, no. 1 (2022): 33–49. http://dx.doi.org/10.3366/ijhac.2022.0275.

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Today, the majority of music performers and vocalists are not able to read mensural notation fluently, and so conductors and music ensembles require modern editions for performing historical music. However, the conversion of printed white mensural sheet music into audible and performable modern notation currently requires elaborate manual editing by specialized music scholars. To close this gap, the present research proposes an algorithm that automatically converts scanned music score sheets of that historic period (the sixteenth and seventeenth centuries) into a file format that is readable i

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Pacha, Alexander, Jan Hajič, and Jorge Calvo-Zaragoza. "A Baseline for General Music Object Detection with Deep Learning." Applied Sciences 8, no. 9 (2018): 1488. http://dx.doi.org/10.3390/app8091488.

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Deep learning is bringing breakthroughs to many computer vision subfields including Optical Music Recognition (OMR), which has seen a series of improvements to musical symbol detection achieved by using generic deep learning models. However, so far, each such proposal has been based on a specific dataset and different evaluation criteria, which made it difficult to quantify the new deep learning-based state-of-the-art and assess the relative merits of these detection models on music scores. In this paper, a baseline for general detection of musical symbols with deep learning is presented. We c

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Vania, Stella, Patrick Sutanto, Ricky Sutanto, and Joan Santoso. "Ekstraksi Partitur Balok Monofonik untuk Instrumen Flute dengan CRNN dan CRF." Journal of Intelligent System and Computation 5, no. 1 (2023): 01–09. http://dx.doi.org/10.52985/insyst.v5i1.218.

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Notasi partitur balok bukanlah notasi yang mudah dibaca oleh pemula dalam dunia musik. Di sinilah Optical Music Recognition (OMR) dapat berperan. OMR merupakan sebuah pembelajaran mengenai komputer yang dapat mengenali objek dalam partitur balok. Dengan adanya program yang menerapkan OMR dan memberikan output dengan format yang mudah dipahami oleh pengguna, maka pemula dalam dunia musik dapat terbantu dalam membaca partitur not balok. Karya ilmiah ini dibuat dengan pendekatan deep learning dalam beberapa arsitektur. Dataset yang digunakan adalah Camera-PrIMuS yang terdiri dari dataset gambar s

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Rizo Valero, David, Nieves Pascual León, and Craig Stuart Sapp. "White Mensural Manual Encoding: from Humdrum to MEI." Cuadernos de Investigación Musical, no. 6 (January 16, 2019): 373. http://dx.doi.org/10.18239/invesmusic.v0i6.1953.

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<p><span lang="EN-US">The recovery of musical heritage currently necessarily involves its digitalization, not only by scanning images, but also by the encoding in computer-readable formats of the musical content described in the original manuscripts. In general, this encoding can be done using automated tools based with what is named Optical Music Recognition (OMR), or manually writing directly the corresponding computer code. The OMR technology is not mature enough yet to extract the musical content of sheet music images with enough quality, and even less from handwritten sources,

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Hakim, Dzikry Maulana, and Ednawati Rainarli. "Convolutional Neural Network untuk Pengenalan Citra Notasi Musik." Techno.Com 18, no. 3 (2019): 214–26. http://dx.doi.org/10.33633/tc.v18i3.2387.

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Optical Music Recognition (OMR) adalah suatu cara untuk melakukan pengenalan pada notasi musik secara otomatis. Masalah utama dalam pendeteksian notasi musik adalah bagaimana sistem dapat mendeteksi sebuah notasi musik dan kemudian mengenali notasi musik tersebut. Notasi musik yang telah dikenali oleh mesin dapat dimanfaatkan untuk diproses kembali menjadi suara. Pada penelitian ini, proses segmentasi dilakukan untuk memotong setiap notasi. Untuk pengenalan notasi musik digunakan Convolutional Neural Network (CNN). Arsitektur CNN yang dipakai adalah kernel 3x3, jumlah layer pada feature learni

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Wang, Qi, Li Zhou, and Xin Chen. "Kernel Density Estimation and Convolutional Neural Networks for the Recognition of Multi-Font Numbered Musical Notation." Electronics 11, no. 21 (2022): 3592. http://dx.doi.org/10.3390/electronics11213592.

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Optical music recognition (OMR) refers to converting musical scores into digitized information using electronics. In recent years, few types of OMR research have involved numbered musical notation (NMN). The existing NMN recognition algorithm is difficult to deal with because the numbered notation font is changing. In this paper, we made a multi-font NMN dataset. Using the presented dataset, we use kernel density estimation with proposed bar line criteria to measure the relative height of symbols, and an accurate separation of melody lines and lyrics lines in musical notation is achieved. Furt

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Aukkhosuwan, Wuttichon, and Wannarat Suntiamorntut. "AI System Design for Robotic Hand to Play the Piano." ASEAN Journal of Scientific and Technological Reports 25, no. 3 (2022): 59–68. http://dx.doi.org/10.55164/ajstr.v25i3.246950.

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Robotic and Artificial Intelligent (AI) have been introduced as a key factor for industry revolution4.0. Many industries, such as manufacturing, agriculture, logistics, supply chain, and so on, are transformed and applied robotic and AI to enhance productivity and reduce cost. AI in creative work is very challenging, especially in music. This paper presents a system to enable the robotic arm to play piano notes with minimal errors. We used the knowledge of Optical music recognition (OMR), Automatic music transcription (AMT), Music source separation (MSS), and the elimination of robot arm cycle

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Wick, Christoph, Alexander Hartelt, and Frank Puppe. "Staff, Symbol and Melody Detection of Medieval Manuscripts Written in Square Notation Using Deep Fully Convolutional Networks." Applied Sciences 9, no. 13 (2019): 2646. http://dx.doi.org/10.3390/app9132646.

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Even today, the automatic digitisation of scanned documents in general, but especially the automatic optical music recognition (OMR) of historical manuscripts, still remains an enormous challenge, since both handwritten musical symbols and text have to be identified. This paper focuses on the Medieval so-called square notation developed in the 11th–12th century, which is already composed of staff lines, staves, clefs, accidentals, and neumes that are roughly spoken connected single notes. The aim is to develop an algorithm that captures both the neumes, and in particular its melody, which can

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Bolya, Mátyás. "AI-SUPPORTED PROCESSING OF HANDWRITTEN TRANSCRIPTIONS FOR HUNGARIAN FOLK SONGS IN A DIGITAL ENVIRONMENT." Ethnomusic 18, no. 1 (2022): 65–82. http://dx.doi.org/10.33398/2523-4846-2022-18-1-65-82.

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My research focuses on creating an AI-supported Digital Research Environment (DRE) that helps analysing and systematizing folk music tunes with the help of the latest information theory and database management results. The study may be ex- tended to the entire source material accumulated by researchers so far, thus inte- grating Hungarian ethnomusicology results of the last hundred years. In this way, new dimensions of structural analysis open up and a large amount of information can be processed that already exceeds the limits of human musical memory. Previous computerized music analysis expe

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Bolya, Mátyás. "AI-SUPPORTED PROCESSING OF HANDWRITTEN TRANSCRIPTIONS FOR HUNGARIAN FOLK SONGS IN A DIGITAL ENVIRONMENT." Ethnomusic 18, no. 1 (2022): 65–82. http://dx.doi.org/10.33398/2523-4846-2022-18-2-65-82.

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My research focuses on creating an AI-supported Digital Research Environment (DRE) that helps analysing and systematizing folk music tunes with the help of the latest information theory and database management results. The study may be ex- tended to the entire source material accumulated by researchers so far, thus inte- grating Hungarian ethnomusicology results of the last hundred years. In this way, new dimensions of structural analysis open up and a large amount of information can be processed that already exceeds the limits of human musical memory. Previous computerized music analysis expe

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de la Fuente, Carlos, Jose J. Valero-Mas, Francisco J. Castellanos, and Jorge Calvo-Zaragoza. "Multimodal image and audio music transcription." International Journal of Multimedia Information Retrieval 11, no. 1 (2021): 77–84. http://dx.doi.org/10.1007/s13735-021-00221-6.

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AbstractOptical Music Recognition (OMR) and Automatic Music Transcription (AMT) stand for the research fields that aim at obtaining a structured digital representation from sheet music images and acoustic recordings, respectively. While these fields have traditionally evolved independently, the fact that both tasks may share the same output representation poses the question of whether they could be combined in a synergistic manner to exploit the individual transcription advantages depicted by each modality. To evaluate this hypothesis, this paper presents a multimodal framework that combines t

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Ali, Eman R., and Narjis M. Shati. "Evaluation System for Multiple-Choice Questions Using Optical Mark Recognition: A Survey." Journal of Al-Qadisiyah for Computer Science and Mathematics 17, no. 1 (2025): 202–13. https://doi.org/10.29304/jqcsm.2025.17.11975.

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performing bulk assessment corrections across various domains and applications can be an expensive and time-consuming task. Optical Mark Recognition (OMR) technology can be used to speed up this process. It is an automated data input method that captures the existence or absence of different marks (filled circles, crosses, and ticks) on printed papers, such as multiple-choice exams. OMR was originally introduced as a dedicated hardware solution and has since evolved into software solutions. However, many of these software solutions lack flexibility, particularly for the end users. This work re

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Hussein Hasan, Rusul, Inaam Salman Aboud, Rasha Majid Hassoon, and Ali saif aldeen Aubaid Khioon. "Optical Mark Recognition using Modify Bi-directional Associative Memory." Tikrit Journal of Pure Science 29, no. 1 (2024): 174–84. http://dx.doi.org/10.25130/tjps.v29i1.1454.

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Optical Mark Recognition (OMR) is an important technology for applications that require speedy, high-accuracy processing of a huge volume of hand-filled forms. The aim of this technology is to reduce manual work, human effort, high accuracy in assessment, and minimize time for evaluation answer sheets. This paper proposed OMR by using Modify Bidirectional Associative Memory (MBAM), MBAM has two phases (learning and analysis phases), it will learn on the answer sheets that contain the correct answers by giving its own code that represents the number of correct answers, then detection marks from

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Schultz, Jacob D., Colin G. White-Dzuro, Cheng Ye, et al. "Extracting Medical Information from Paper COVID-19 Assessment Forms." Applied Clinical Informatics 12, no. 01 (2021): 170–78. http://dx.doi.org/10.1055/s-0041-1723024.

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Abstract Objective This study examines the validity of optical mark recognition, a novel user interface, and crowdsourced data validation to rapidly digitize and extract data from paper COVID-19 assessment forms at a large medical center. Methods An optical mark recognition/optical character recognition (OMR/OCR) system was developed to identify fields that were selected on 2,814 paper assessment forms, each with 141 fields which were used to assess potential COVID-19 infections. A novel user interface (UI) displayed mirrored forms showing the scanned assessment forms with OMR results superimp

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S, Ravindra. "Automated OMR Analyser using ML and Image Processing." International Journal for Research in Applied Science and Engineering Technology 13, no. 6 (2025): 1843–45. https://doi.org/10.22214/ijraset.2025.72535.

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This work explores Optical Mark Recognition (OMR) systems built on Machine Learning and Image Processing techniques to achieve accurate and automated evaluation of answer sheets. The primary objective is to eliminate dependency on specialized scanners by utilizing devices such as mobile cameras and webcams. This system processes scanned answer sheets by isolating responses and matching them with reference answers using automated logic. Several models integrate contour detection, circle detection, and error-tolerant classification to ensure robustness even with imperfect markings. The survey sh

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Luciano, Ruth G. "Innovative test item analysis using optical mark recognition technology: An evaluation." International Journal of ADVANCED AND APPLIED SCIENCES 12, no. 4 (2025): 1–11. https://doi.org/10.21833/ijaas.2025.04.001.

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This study focuses on the need for effective tools to improve assessment processes in education by developing and evaluating a software application that analyzes test items using Optical Mark Recognition (OMR) technology. Traditional methods of test item analysis are often slow and unreliable due to manual handling and limited statistical insights. The proposed software aims to automate the creation, analysis, and management of test items, making the process more efficient for educators. The study follows a mixed-method approach, using qualitative methods for software design and quantitative e

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Ha, Tran Vu, and Nguyen Thi Thu. "An Application of Image Processing in Optical Mark Recognition." Vietnam Journal of Agricultural Sciences 3, no. 4 (2020): 864–71. http://dx.doi.org/10.31817/vjas.2020.3.4.09.

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The Optical Mark Recognition (OMR) is very popular with universities for the reading of multiple-choice questions. In this article, we presented a software system for processing surveys at the Vietnam National University of Agriculture based on digital image processing. This software was built using MATLAB and easy to use. The surveys were digitized using a scanner and sent to the software tool. In this study, we tested more than 170 surveys of nine different types. The software tool correctly detected all the valid answers. It was also able to detect all questions with no or multiple marks.

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Cuerdo, Ronnel, Michael Jomar B. Ison, and Christian Diols T. Oñate. "Effectiveness of Automation in Evaluating Test Results Using EvalBee as an Alternative Optical Mark Recognition (OMR): A Quantitative-Evaluative Approach from a Philippine Public School." International Journal of Theory and Application in Elementary and Secondary School Education 3, no. 2 (2021): 61–75. http://dx.doi.org/10.31098/ijtaese.v3i2.661.

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AbstractWithin this study, the authors want to address the problem of overworking of teachers in Philippine schools due to their excessive clerical responsibility, which could lead to teacher attrition. The authors propose to automate the process, particularly the evaluation of student test results since it could improve human well-being by reducing the burden of manual labor. Automation using OMR has not been widely applied in Philippine schools due to cost issues. The authors want to observe whether an alternative OMR - EvalBee - can meet the evaluation standard even though it is free of cha

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Calvo-Zaragoza, Jorge, Jan Hajič Jr., and Alexander Pacha. "Understanding Optical Music Recognition." ACM Computing Surveys 53, no. 4 (2020): 1–35. http://dx.doi.org/10.1145/3397499.

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Hermawan, Moh Novi. "DETEKSI LEMBAR JAWABAN KOMPUTER MENGGUNAKAN OMR (OPTICAL MARK RECOGNITION) DI MTS NURUL IMAN." JATISI (Jurnal Teknik Informatika dan Sistem Informasi) 8, no. 3 (2021): 1361–72. http://dx.doi.org/10.35957/jatisi.v8i3.1078.

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Conventional exams or manual exams were implemented decades ago and are still used today. This type of test uses a writing instrument as a test medium, namely the test is carried out in the form of general stationery such as paper, pencil, and pen, the questions and answers to the test are written by hand. One way to assess the success of the teaching process in schools is to carry out exams. In the implementation of the exam at MTS Nurul Iman, he used a computer answer sheet as an entry. Meanwhile, schools are required to have certain scanners that are expensive to correct computer answer she

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Baró, Arnau, Pau Riba, Jorge Calvo-Zaragoza, and Alicia Fornés. "From Optical Music Recognition to Handwritten Music Recognition: A baseline." Pattern Recognition Letters 123 (May 2019): 1–8. http://dx.doi.org/10.1016/j.patrec.2019.02.029.

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Rizki Pratama, Muhamad, and Isa Faqihuddin Hanif. "Implementasi Metode Canny dalam Deteksi Tepi pada Aplikasi OMR (Optical Mark Recognition) Menggunakan Pengembangan Sistem Waterfall." Edunity : Kajian Ilmu Sosial dan Pendidikan 2, no. 2 (2023): 262–78. http://dx.doi.org/10.57096/edunity.v2i2.60.

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Pengolahan citra digital merupakan suatu teknologi input berupa gambar seperti foto atau video yang dapat dimanfaatkan dalam meningkatkan kualitas suatu citra, sedangkan output dari pengolahan citra visual dapat berupa gambar atau sejumlah karakteristik yang berkaitan dengan sebuah gambar. Serta proses dalam identifikasi citra tersebut dibantu dengan menggunakan beberapa metode dengan maksud mengembangkan sistem yang bersumber pada citra tersebut. Dalam hal ini, penerapan teknik pengolahan citra digital akan digunakan pada objek lembar jawaban untuk mempercepat dan mempermudah proses mengoreks

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Chen, Liang, and Christopher Raphael. "Human-Directed Optical Music Recognition." Electronic Imaging 2016, no. 17 (2016): 1–9. http://dx.doi.org/10.2352/issn.2470-1173.2016.17.drr-053.

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Rebelo, A., G. Capela, and Jaime S. Cardoso. "Optical recognition of music symbols." International Journal on Document Analysis and Recognition (IJDAR) 13, no. 1 (2009): 19–31. http://dx.doi.org/10.1007/s10032-009-0100-1.

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Bellini, Pierfrancesco, Ivan Bruno, and Paolo Nesi. "Assessing Optical Music Recognition Tools." Computer Music Journal 31, no. 1 (2007): 68–93. http://dx.doi.org/10.1162/comj.2007.31.1.68.

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Jadhav, Madhuri Aravind. "TestMaster Insights." International Journal for Research in Applied Science and Engineering Technology 13, no. 3 (2025): 704–14. https://doi.org/10.22214/ijraset.2025.67349.

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The TestMaster Insights project is multiple-choice question (MCQ) assessment system designed to streamline the examination process. It integrates Optical Mark Recognition (OMR) technology to accurately capture student responses from answer sheets. The system ensures seamless question display, real-time answer checking, and result computation, reducing manual effort and enhancing efficiency. The core functionality includes the automatic generation and display of MCQs, where candidates respond using OMR sheets. The system scans and interprets marked answers, comparing them with the correct respo

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Andrea, Andrea, Paoline Paoline, and Amalia Zahra. "Music Note Position Recognition in Optical Music Recognition using Convolutional Neural Network." International Journal of Arts and Technology 13, no. 1 (2021): 1. http://dx.doi.org/10.1504/ijart.2021.10035633.

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Andrea, N. A., N. A. Paoline, and Amalia Zahra. "Music note position recognition in optical music recognition using convolutional neural network." International Journal of Arts and Technology 13, no. 1 (2021): 45. http://dx.doi.org/10.1504/ijart.2021.115764.

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Lainez, Sheryl May D., Leo Joshua Q. Gresos, and Via Karen A. Maganggo. "An Automated Entrance Examination Checker Using Optical Mark Recognition." Journal of Computer, Software, and Program 1, no. 1 (2024): 8–13. http://dx.doi.org/10.69739/jcsp.v1i1.43.

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Optical Mark Recognition (OMR) serves as a valuable data entry tool, especially in education and testing, by capturing human-marked data from document forms like surveys and tests. This paper presented an automated system for expeditiously and accurately checking the entrance examinations of new students, streamlining the transaction process for university freshmen enrollees. This transition from manual to automated assessment or grading expedites the checking of a 250-item multiple choice exam. The system comprises two main components: hardware and software. The hardware component integrates

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Sheryl, May D. Lainez, Joshua Q. Gresos Leo, and Karen A. Maganggo Via. "An Automated Entrance Examination Checker Using Optical Mark Recognition." Journal of Computer, Software, and Program 1, no. 1 (2024): 8–13. https://doi.org/10.69739/jcsp.v1i1.43.

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Optical Mark Recognition (OMR) serves as a valuable data entry tool, especially in education and testing, by capturing human-marked data from document forms like surveys and tests. This paper presented an automated system for expeditiously and accurately checking the entrance examinations of new students, streamlining the transaction process for university freshmen enrollees. This transition from manual to automated assessment or grading expedites the checking of a 250-item multiple choice exam. The system comprises two main components: hardware and software. The hardware component integrates

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