Worddeepnet: handwritten gurumukhi word recognition using convolutional neural network

Kaur, Harmandeep; Bansal, Shally; Kumar, Munish; Mittal, Ajay; Kumar, Kishor

doi:10.1007/s11042-023-15527-2

Cited by 3 publications

(2 citation statements)

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“…In [81], the authors introduced a CNN based approach called Worddeepnet to identify words from handwritten Gurumukhi text focusing on word level. The approach holistically segments words.…”

Section: Cnnmentioning

confidence: 99%

Handwritten Recognition Techniques: A Comprehensive Review

Alhamad,

Shehab,

Shambour

et al. 2024

Symmetry

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Given the prevalence of handwritten documents in human interactions, optical character recognition (OCR) for documents holds immense practical value. OCR is a field that empowers the translation of various document types and images into data that can be analyzed, edited, and searched. In handwritten recognition techniques, symmetry can be crucial to improving accuracy. It can be used as a preprocessing step to normalize the input data, making it easier for the recognition algorithm to identify and classify characters accurately. This review paper aims to summarize the research conducted on character recognition for handwritten documents and offer insights into future research directions. Within this review, the research articles focused on handwritten OCR were gathered, synthesized, and examined, along with closely related topics, published between 2019 and the first quarter of 2024. Well-established electronic databases and a predefined review protocol were utilized for article selection. The articles were identified through keyword, forward, and backward reference searches to comprehensively cover all relevant literature. Following a rigorous selection process, 116 articles were included in this systematic literature review. This review article presents cutting-edge achievements and techniques in OCR and underscores areas where further research is needed.

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“…In [81], the authors introduced a CNN based approach called Worddeepnet to identify words from handwritten Gurumukhi text focusing on word level. The approach holistically segments words.…”

Section: Cnnmentioning

confidence: 99%

Handwritten Recognition Techniques: A Comprehensive Review

Alhamad,

Shehab,

Shambour

et al. 2024

Symmetry

View full text Add to dashboard Cite

show abstract

“…Therefore, in the literature, two main approaches exist for license plate detection (LPD) which are the traditional techniques, and the deep learning methods The first approach uses contour analysis, thresholding, and edge detection to find the license plate region in an image. As for the second technique, CNNs 6 remains a popular choice for detecting license plates. Popular real-time license plate identification designs are SSD which refers to Single Shot MultiBox Detector 7 and YOLO.…”

Section: Related Workmentioning

confidence: 99%

Enhancing automated vehicle identification by integrating YOLO v8 and OCR techniques for high-precision license plate detection and recognition

Moussaoui,

Akkad,

Benslimane

et al. 2024

Sci Rep

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Vehicle identification systems are vital components that enable many aspects of contemporary life, such as safety, trade, transit, and law enforcement. They improve community and individual well-being by increasing vehicle management, security, and transparency. These tasks entail locating and extracting license plates from images or video frames using computer vision and machine learning techniques, followed by recognizing the letters or digits on the plates. This paper proposes a new license plate detection and recognition method based on the deep learning YOLO v8 method, image processing techniques, and the OCR technique for text recognition. For this, the first step was the dataset creation, when gathering 270 images from the internet. Afterward, CVAT (Computer Vision Annotation Tool) was used to annotate the dataset, which is an open-source software platform made to make computer vision tasks easier to annotate and label images and videos. Subsequently, the newly released Yolo version, the Yolo v8, has been employed to detect the number plate area in the input image. Subsequently, after extracting the plate the k-means clustering algorithm, the thresholding techniques, and the opening morphological operation were used to enhance the image and make the characters in the license plate clearer before using OCR. The next step in this process is using the OCR technique to extract the characters. Eventually, a text file containing only the character reflecting the vehicle's country is generated. To ameliorate the efficiency of the proposed approach, several metrics were employed, namely precision, recall, F1-Score, and CLA. In addition, a comparison of the proposed method with existing techniques in the literature has been given. The suggested method obtained convincing results in both detection as well as recognition by obtaining an accuracy of 99% in detection and 98% in character recognition.

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