Headline Based Text Extraction from Outdoor Images

Ghoshal, Ranjit; Roy, Anandarup; Bhowmik, Tanmay; Parui, Susanta Kumar

doi:10.1007/978-3-642-21786-9_72

Cited by 8 publications

(5 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Their achieved precision and recall values were 68.8% and 71.2% respectively considering a set of 100 images. R. Ghoshal et al [12] proposed a morphological approach for Bangla text extraction from images. Their approach was limited to highlighted texts only.…”

Section: Related Workmentioning

confidence: 99%

Extraction and Recognition of Bangla Texts from Natural Scene Images Using CNN

Islam

Talukder

2020

Lecture Notes in Computer Science

View full text Add to dashboard Cite

The semantic information presents in the scene images may be the useful information for the viewers who is searching for a specific location or any specific shop and address. This type of information can also be useful in licenseplate detection, controlling the vehicle on the road, robot navigation, and assisting visually impaired persons. An efficient method is presented in this paper to detect and extract Bangla texts from scene images based on a connected component approach along with rule-based filtering and vertical scanning scheme. Next, extracted characters are recognized by using Convolutional Neural Network (CNN). The method consists of the four basic consecutive steps such as detection and extraction of the Region of Interest (ROI), segmentation of the words, extraction of characters, and recognition of the extracted characters. After extracting the ROI from the input image, connected component(CC) analysis and bounding box technology are used for segmentation of Bangla words. To separate and extract Bangla characters from the segmented Bangla words, vertical scanning based method along with a dynamic threshold value has been applied. Finally, character recognition is carried out using CNN. The proposed algorithm is applied to 600 scene images of different writing styles and colors, and we have obtained 89.25% accuracy in text detection and 94.50% accuracy in the extraction of characters. We have achieved an accuracy of 99.30% and 95.76% in recognition of Bangla digits and characters respectively. By combining both the digits and characters, obtained recognition accuracy is 95.39%.

show abstract

Section: Related Workmentioning

confidence: 99%

Extraction and Recognition of Bangla Texts from Natural Scene Images Using CNN

Islam

Talukder

2020

Lecture Notes in Computer Science

View full text Add to dashboard Cite

show abstract

“…Their algorithm fails if there is small sized curved text present in the image. Another morphological approach was proposed by Ghoshal et al [10]. In their proposed method, at first, they detected unattached text components and then segmented connected components from an image containing Bangla/Devanagari characters.…”

Section: Related Workmentioning

confidence: 99%

An Enhanced MSER Pruning Algorithm for Detection and Localization of Bangla Texts from Scene Images

Islam

Talukder

2019

IAJIT

View full text Add to dashboard Cite

Text detection and localization have great importance for content based image analysis and text based image indexing. The efficiency of text recognition depends on the efficiency of text localization. So, the main goal of the proposed method is to detect and localize text regions with high accuracy. To achieve this goal, a new and efficient method has been introduced for localization of Bangla text from scene images. In order to improve precision and recall as well as f-measure, Maximally Stable Extremal Region (MSER) based method along with double filtering techniques have been used. As MSER algorithm generates many false positives, we have introduced double filtering method for removing these false positives to increase the f-measure to a great extent. Our proposed method works at three basic levels. Firstly, MSER regions are generated from the input color image by converting it into gray scale image. Secondly, some heuristic features are used to filter out most of the false positives or non-text regions. Lastly, Stroke Width Transform (SWT) based filtering method is used to filter out remaining non-text regions. Remaining components are then grouped into candidate text regions marked by bounding box over each region. As there is no benchmark database for Bangla text, the proposed method is implemented on our own prepared database consisting of 200 scene images of Bangla texts and has got prominent performance. To evaluate the performance of our proposed approach, we have also tested the proposed method on International Conference on Document Analysis and Recognition( ICDAR) 2013 benchmark database and have got a better result than the related existing methods.

show abstract

“…Captured images of head lighted text in Bengali suffer from proper character segmentation also. Another morphological approach was proposed by Ghoshal et al [4] based on detecting unattached text area and segmenting connected components of Bengali/Devnagari characters from an image. Their online and automated system of Bengali character recognition was based on calculating features of connected components such as elongations ratio, number of holes present in the segmented character, aspect ratio and object to background pixels ratio.…”

Section: Past Workmentioning

confidence: 99%