Combining Image Processing Techniques, OCR, and OMR for the Digitization of Musical Books

Santamaría, Gonzalo; Pérez, César Domínguez; Heras, Jónathan; Mata, Eloy; Pascual, Vico

doi:10.1007/978-3-031-06555-2_37

Cited by 2 publications

(3 citation statements)

References 21 publications

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“…A study by Semkovych and Shymanskyi demonstrates the effectiveness of a method that combines CRNN, LSTM, and CTC. This method, though simple, outperforms standalone CNN/RNN approaches by approximately 15% and can decode handwritten text using low-power devices [14]. SVTR [15] proposes a unified visual model for recognizing scene text that balances inference speed and accuracy.…”

Section: Ocr (Optical Character Recognition)mentioning

confidence: 99%

Unstructured Document Information Extraction Method with Multi-Faceted Domain Knowledge Graph Assistance for M2M Customs Risk Prevention and Screening Application

Tian,

Wang,

Wan

et al. 2024

Electronics

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As a crucial national security defense line, the existing risk prevention and screening system of customs falls short in terms of intelligence and diversity for risk identification factors. Hence, the urgent issues to be addressed in the risk identification system include intelligent extraction technology for key information from Customs Unstructured Accompanying Documents (CUADs) and the reliability of the extraction results. In the customs scenario, OCR is employed for M2M interactions, but current models have difficulty adapting to diverse image qualities and complex customs document content. We propose a hybrid mutual learning knowledge distillation (HMLKD) method for optimizing a pre-trained OCR model’s performance against such challenges. Additionally, current models lack effective incorporation of domain-specific knowledge, resulting in insufficient text recognition accuracy for practical customs risk identification. We propose a customs domain knowledge graph (CDKG) developed using CUAD knowledge and propose an integrated CDKG post-OCR correction method (iCDKG-PostOCR) based on CDKG. The results on real data demonstrate that the accuracies improve for code text fields to 97.70%, for character type fields to 96.55%, and for numerical type fields to 96.00%, with a confidence rate exceeding 99% for each. Furthermore, the Customs Health Certificate Extraction System (CHCES) developed using the proposed method has been implemented and verified at Tianjin Customs in China, where it has showcased outstanding operational performance.

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Section: Ocr (Optical Character Recognition)mentioning

confidence: 99%

Unstructured Document Information Extraction Method with Multi-Faceted Domain Knowledge Graph Assistance for M2M Customs Risk Prevention and Screening Application

Tian,

Wang,

Wan

et al. 2024

Electronics

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“…Its integration with YOLOv5 improves recognition speed and accuracy [21]. Based on a study [22], OCR was of great benefit in its application to text along with optical mark recognition (OMR) for the digitization of music books [23].…”

Section: Optical Character Recognition (Ocr)mentioning

confidence: 99%

“…Its integration with YOLOv5 improv and accuracy [21]. Based on a study [22], OCR was of great benefi text along with optical mark recognition (OMR) for the digitiza [23]. In the subsequent preprocessing phase, a suite of techniqu compassing image alignment, noise reduction, and language iden deavours collectively aim to refine the image by eliminating im hancing its readiness for subsequent recognition.…”

Section: Optical Character Recognition (Ocr)mentioning

confidence: 99%

Recognition of Arabic Air-Written Letters: Machine Learning, Convolutional Neural Networks, and Optical Character Recognition (OCR) Techniques

Nahar,

Alsmadi,

Al Mamlook

et al. 2023

Sensors

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Air writing is one of the essential fields that the world is turning to, which can benefit from the world of the metaverse, as well as the ease of communication between humans and machines. The research literature on air writing and its applications shows significant work in English and Chinese, while little research is conducted in other languages, such as Arabic. To fill this gap, we propose a hybrid model that combines feature extraction with deep learning models and then uses machine learning (ML) and optical character recognition (OCR) methods and applies grid and random search optimization algorithms to obtain the best model parameters and outcomes. Several machine learning methods (e.g., neural networks (NNs), random forest (RF), K-nearest neighbours (KNN), and support vector machine (SVM)) are applied to deep features extracted from deep convolutional neural networks (CNNs), such as VGG16, VGG19, and SqueezeNet. Our study uses the AHAWP dataset, which consists of diverse writing styles and hand sign variations, to train and evaluate the models. Prepossessing schemes are applied to improve data quality by reducing bias. Furthermore, OCR character (OCR) methods are integrated into our model to isolate individual letters from continuous air-written gestures and improve recognition results. The results of this study showed that the proposed model achieved the best accuracy of 88.8% using NN with VGG16.

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Combining Image Processing Techniques, OCR, and OMR for the Digitization of Musical Books

Cited by 2 publications

References 21 publications

Unstructured Document Information Extraction Method with Multi-Faceted Domain Knowledge Graph Assistance for M2M Customs Risk Prevention and Screening Application

Unstructured Document Information Extraction Method with Multi-Faceted Domain Knowledge Graph Assistance for M2M Customs Risk Prevention and Screening Application

Recognition of Arabic Air-Written Letters: Machine Learning, Convolutional Neural Networks, and Optical Character Recognition (OCR) Techniques

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