A new wavelet-Laplacian method for arbitrarily-oriented character segmentation in video text lines

Liang, Guozhu; Shivakumara, Palaiahnakote; Lü, Tong; Tan, Chew Lim

doi:10.1109/icdar.2015.7333897

Cited by 12 publications

(2 citation statements)

References 10 publications

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“…Fernandes et al [3] presented a k-means clustering algorithm for the segmentation of LP characters and then connected components labeling analysis (CCLA) algorithm is used to identify the connected pixel regions and grouping the suitable pixels into components for the extraction of every character in an effective way. Liang et al [10] employed a novel wavelet Laplacian technique to segment the characters randomly from video text lines. It searches for zero crossing points to explore space among words as well as characters.…”

Section: B Character Segmentationmentioning

confidence: 99%

Automatic Vehicle License Plate Recognition Using Optimal K-Means With Convolutional Neural Network for Intelligent Transportation Systems

et al. 2020

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Due to recent developments in highway research and increased utilization of vehicles, there has been significant interest paid on latest, effective, and precise Intelligent Transportation System (ITS). The process of identifying particular objects in an image plays a crucial part in the fields of computer vision or digital image processing. Vehicle License Plate Recognition (VLPR) process is a challenging process because of variations in viewpoint, shape, color, multiple formats and non-uniform illumination conditions at the time of image acquisition. This paper presents an effective deep learning-based VLPR model using optimal K-means (OKM) clustering-based segmentation and Convolutional Neural Network (CNN) based recognition called OKM-CNN model. The proposed OKM-CNN model operates on three main stages namely License Plate (LP) detection, segmentation using OKM clustering technique and license plate number recognition using CNN model. During first stage, LP localization and detection process take place using Improved Bernsen Algorithm (IBA) and Connected Component Analysis (CCA) models. Then, OKM clustering with Krill Herd (KH) algorithm get executed to segment the LP image. Finally, the characters in LP get recognized with the help of CNN model. An extensive experimental investigation was conducted using three datasets namely Stanford Cars, FZU Cars and HumAIn 2019 Challenge dataset. The attained simulation outcome ensured effective performance of the OKM-CNN model over other compared methods in a considerable way.

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Section: B Character Segmentationmentioning

confidence: 99%

Automatic Vehicle License Plate Recognition Using Optimal K-Means With Convolutional Neural Network for Intelligent Transportation Systems

et al. 2020

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“…Sharma et al [15] proposed a character segmentation method that is sensitive to dominant points for multi-oriented video. Liang et al [16] proposed a novel wavelet Laplacian method to segment the characters with arbitrary orientation. This method explores zero crossing points to find spaces between words or characters.…”

Section: Related Workmentioning

confidence: 99%

TSER: A Two-Stage Character Segmentation Network With Two-Stream Attention and Edge Refinement

et al. 2020

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Segmenting characters in an image is a classic yet challenging task in computer vision. Correctly determining boundaries of adhesive characters with various scales and shapes is essential for character segmentation, especially for separating handwritten characters. Nevertheless, there is seldom work in the literature which can achieve satisfactory performance. In this article, by leveraging the ability of deep neural networks, we proposed a two-stage character segmentation network with two-stream attention and edge refinement (TSER) to tackle this problem. TSER firstly locates every character by object detection, then extracts their corresponding contours. In the process, a novel two-stream attention mechanism (TSAM) is proposed to make the network focus more on the discrepancy of character boundaries. Furthermore, a novel generating method is used to dynamically generate anchors on different feature levels to improve model's sensitivity on the shapes and scales of characters. Eventually a cascaded edge refinement network is used to obtain contour of each character. To prove the efficiency and generalization ability of our model, we compared TSER with traditional algorithms and other deep learning models on two commonly used datasets in different segmentation tasks. The comparative result indicated that TSER reached state-of-the-art performance.

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Transform-Based Text Detection Approach in Images

Naveena¹,

Ajay

Aradhya

2019

Advances in Intelligent Systems and Computing

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A new wavelet-Laplacian method for arbitrarily-oriented character segmentation in video text lines

Cited by 12 publications

References 10 publications

Automatic Vehicle License Plate Recognition Using Optimal K-Means With Convolutional Neural Network for Intelligent Transportation Systems

Automatic Vehicle License Plate Recognition Using Optimal K-Means With Convolutional Neural Network for Intelligent Transportation Systems

TSER: A Two-Stage Character Segmentation Network With Two-Stream Attention and Edge Refinement

Transform-Based Text Detection Approach in Images

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