Pseudo Zernike moments-based approach for text detection and localisation from lecture videos

Belkacem, Soundes; Guezouli, Larbi; Zidat, Samir

doi:10.1504/ijcse.2019.100231

Cited by 3 publications

(2 citation statements)

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“…Soundes et al [30] have proposed an approach that partition the video frame into equal sized windows for calculating pseudo Zernike moments, which act as local features. The k-means technique is applied on the detected features to cluster them.…”

Section: Related Workmentioning

confidence: 99%

An Efficient Projection Screen Detection from Presentation Videos using CustomYOLOv7 Object Detection Method

Purushotham,

Ramani,

Bindu

2024

Preprint

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Lecture videos are analyzed for the development of various applications that involves indexing, summarization, content extraction, search, and navigation. Lecture videos captured in classrooms and conference rooms has digital slides projected on to the screen on stage. Projection screen detection is a crucial task for the extraction of slide region in such presentation videos. In this paper, we present an interesting approach for detecting the location of slide region in video frames using the You Only Look Once (YOLO) object detection model. First we train the custom YOLOv7 model on a labelled dataset of frames from presentation videos showing projected slides and then the trained model is applied on new images that are not used in training to predict the location of projector screens. We collected and annotated over 2000 frames from various presentation videos and then various augmented techniques are applied to prepare a dataset of 5000 images. We evaluated this method on our custom dataset and the results are compared with other popular object detection methods. Our experiments demonstrated that our custom YOLOv7 model outperforms basic YOLOv7 and Retinanet with regards to accuracy and computational effectiveness. Our results suggest that custom YOLOv7 provides a promising solution for projector screen detection and has the potential to be applied in various practical applications

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Section: Related Workmentioning

confidence: 99%

An Efficient Projection Screen Detection from Presentation Videos using CustomYOLOv7 Object Detection Method

Purushotham,

Ramani,

Bindu

2024

Preprint

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“…The text detection process can enhance the text quality as the overall outcome is based on the performance of the text recognition process. Various techniques such as the connected-component based, edge-based, texture-based, and stroke-based methods are available for enhancing the text detection and recognition processes [5].…”

Section: Introductionmentioning

confidence: 99%

CNN and Fuzzy Rules Based Text Detection and Recognition from Natural Scenes

Mithila¹,

Arunprakash²,

Ramachandran³

2022

Computer Systems Science and Engineering

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In today's real world, an important research part in image processing is scene text detection and recognition. Scene text can be in different languages, fonts, sizes, colours, orientations and structures. Moreover, the aspect ratios and layouts of a scene text may differ significantly. All these variations appear assignificant challenges for the detection and recognition algorithms that are considered for the text in natural scenes. In this paper, a new intelligent text detection and recognition method for detectingthe text from natural scenes and forrecognizing the text by applying the newly proposed Conditional Random Field-based fuzzy rules incorporated Convolutional Neural Network (CR-CNN) has been proposed. Moreover, we have recommended a new text detection method for detecting the exact text from the input natural scene images. For enhancing the presentation of the edge detection process, image pre-processing activities such as edge detection and color modeling have beenapplied in this work. In addition, we have generated new fuzzy rules for making effective decisions on the processes of text detection and recognition. The experiments have been directedusing the standard benchmark datasets such as the ICDAR 2003, the ICDAR 2011, the ICDAR 2005 and the SVT and have achieved better detection accuracy intext detection and recognition. By using these three datasets, five different experiments have been conducted for evaluating the proposed model. And also, we have compared the proposed system with the other classifiers such as the SVM, the MLP and the CNN. In these comparisons, the proposed model has achieved better classification accuracywhen compared with the other existing works.

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Prediction of the Emotion Responses to Poster Designs based on Graphical Features: A Machine learning-driven Approach

Kim¹,

Suk²

2020

adr

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Pseudo Zernike moments-based approach for text detection and localisation from lecture videos

Cited by 3 publications

References 0 publications

An Efficient Projection Screen Detection from Presentation Videos using CustomYOLOv7 Object Detection Method

An Efficient Projection Screen Detection from Presentation Videos using CustomYOLOv7 Object Detection Method

CNN and Fuzzy Rules Based Text Detection and Recognition from Natural Scenes

Prediction of the Emotion Responses to Poster Designs based on Graphical Features: A Machine learning-driven Approach

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