Scene text detection via extremal region based double threshold convolutional network classification

Zhu, Wei; Lou, Jing; Chen, Longtao; Xia, Qingyuan; Ren, Mengye

doi:10.1371/journal.pone.0182227

Cited by 9 publications

(3 citation statements)

References 52 publications

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“…Zhu et al. presented a low-level detector based on MSER and region proposal for text detection [ 34 ]. The heuristic features are then used to group the characters into text lines.…”

Section: Related Workmentioning

confidence: 99%

Structured Cluster Detection from Local Feature Learning for Text Region Extraction

Lin

Hsu

2023

Entropy

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The detection of regions of interest is commonly considered as an early stage of information extraction from images. It is used to provide the contents meaningful to human perception for machine vision applications. In this work, a new technique for structured region detection based on the distillation of local image features with clustering analysis is proposed. Different from the existing methods, our approach takes the application-specific reference images for feature learning and extraction. It is able to identify text clusters under the sparsity of feature points derived from the characters. For the localization of structured regions, the cluster with high feature density is calculated and serves as a candidate for region expansion. An iterative adjustment is then performed to enlarge the ROI for complete text coverage. The experiments carried out for text region detection of invoice and banknote demonstrate the effectiveness of the proposed technique.

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“…Zhu et al. presented a low-level detector based on MSER and region proposal for text detection [ 34 ]. The heuristic features are then used to group the characters into text lines.…”

Section: Related Workmentioning

confidence: 99%

Structured Cluster Detection from Local Feature Learning for Text Region Extraction

Lin

Hsu

2023

Entropy

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“…This operator, thus, represents a good competitor. Different improvements of the MSER operator have been proposed [29,30]. We selected a general implementation of the MSER operator [31] along with the FASText operator [7], which provides adaptation to scene text detection.…”

Section: Comparative Operatorsmentioning

confidence: 99%

Fast RT-LoG operator for scene text detection

Dinh

Delalandre

Conte

2020

J Real-Time Image Proc

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HAL is a multidisciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

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“…In recent years, text detection has become a research hotspot and a challenging topic in the field of computer vision [7][8]. Tutz proposed the Logit model to estimate weights, rather than using weights that are entirely determined by distance.…”

Section: Introductionmentioning

confidence: 99%

Chinese Text Event Detection Technology Based on Improved Neural Network

Cui¹,

Wei²

2021

Frontiers in Artificial Intelligence and Applications

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With the development of deep learning, the use of neural network for text detection has been more in-depth research and more widely used. Based on this, this paper studies the Chinese text event detection technology based on improved neural network. In the research, this paper uses the flower pollination algorithm (FPA) to improve the traditional BP neural network algorithm. By optimizing the weights and thresholds of BP neural network, a Chinese text event detection method based on improved neural network is proposed. In order to verify the effect of the Chinese text event detection method based on improved neural network, this paper compares it with the natural scene text detection method, and compares the recall rate, accuracy rate and time-consuming. The results show that the accuracy rate of the natural scene text detection method is 88%, and the recall rate is 73%. The accuracy rate of the text detection method based on the improved neural network is 95% and the recall rate is 86%. The F value of the natural scene text detection method in the Chinese text event detection test is 0.79, which takes 4.56s The value of F in is 0.90, which takes 0.64s. Therefore, the Chinese text event detection method based on improved neural network has better performance.

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Scene text detection via extremal region based double threshold convolutional network classification

Cited by 9 publications

References 52 publications

Structured Cluster Detection from Local Feature Learning for Text Region Extraction

Structured Cluster Detection from Local Feature Learning for Text Region Extraction

Fast RT-LoG operator for scene text detection

Chinese Text Event Detection Technology Based on Improved Neural Network

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