Extraction of arbitrary text in natural scene image based on stroke width transform

Jameson, Jinjuli; Abdullah, Siti Norul Huda Sheikh

doi:10.1109/isda.2014.7066257

Cited by 7 publications

(6 citation statements)

References 19 publications

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“…3 Flow of proposed work between these two pixels is considered as the stroke width. Edges are then derived from the image and the stroke width is calculated from the gradient features [4,5]. Weak edges generated due to the poor quality images can be handled by adopting other generalize technique like gradient vector flow over manifold (GVFOM) [40].…”

Section: Related Workmentioning

confidence: 99%

Text Region Identification in Indian Street Scene Images Using Stroke Width Transform and Support Vector Machine

Shiravale¹,

Sannakki²,

Jayadevan

2021

SN COMPUT. SCI.

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Detecting the presence of text in street scene images is a very crucial task for many applications and its complexity may vary from script to script due to the unique characteristics of each script. A technique to detect and localize text written in Devanagari script from scene images is presented in this paper. Initially, candidate regions are localized using low-level features like edge and colour. Due to the complex nature of scene images, these regions may contain irrelevant information. Stroke Width Transform (SWT) and geometric features are then extracted from these localized regions for correctly identifying the text regions. An efficient technique is proposed in this paper for the extraction of stroke width from dark text (foreground) on a light background as well as from light text (foreground) on dark background. Methods based on heuristic rules are inefficient for text and non-text identification due to the nonlinearity of extracted features. It has been observed that Support Vector Machines are the most popular and efficient classifiers for text/non-text classification. Also, an attempt is made here to explore other computationally less expensive classifiers like Bayesian due to its simplicity and Decision Tree due to its pure class partitioning power. Hence SVM, Bayesian and Decision Tree classifiers are used for the classification of text and non-text regions and the results are compared. An image dataset containing 1250 scene images has been created for experimentation. It is clear from the experimental results that the technique proposed in this paper outperforms some of the existing techniques in terms of accuracy.

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Section: Related Workmentioning

confidence: 99%

Text Region Identification in Indian Street Scene Images Using Stroke Width Transform and Support Vector Machine

Shiravale¹,

Sannakki²,

Jayadevan

2021

SN COMPUT. SCI.

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“…Stroke width is derived from the target pixel map combined with geometric reasoning called a Stroke width Transform (SWT) map. SWT can be obtained either by gradients derived from the edge map [24] or distance transformation-based approach [25]. In a gradient-based approach, the distance between two pixels that are on the opposite gradient direction of each other is considered as a stroke width.…”

Section: Ivi Stroke Width Transform (Swt)mentioning

confidence: 99%

Recent Advancements in Text Detection Methods from Natural Scene Images

Shiravale¹,

Sannakki²,

Rajpurohit³

2020

International Journal of Engineering Research and Technology

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Effect of digitization and globalization has narrowed down the gap of geographical boundaries. Text/language plays an important role in getting connected with people utilizing oral or written communication. Nowadays text data is easily available in the form of multimedia e.g. audio, videos. A technique is needed to understand and interpret the text present in the videos/images which are rich in contents compared to audio data. Text detection and recognition are two main steps of such text-based applications. Text detection from natural scene images is tedious compared to text detection from document images. Various methods are available for text detection from natural scene images. Text detection methods are generally script specific. The main purpose of this paper is to highlight available text detection methods with pros and cons, challenges in the text detection process, evaluation parameters as well as recent achievements. The paper will act as a roadmap for upcoming researchers to select an appropriate text detection method.

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“…SWT merges pixels of similar stroke width into larger components. We can find the most likely text are by bottom up integration [11,12].…”

Section: Previous Workmentioning

confidence: 99%

Automatic elongated object segmentation in CT image using Stroke Width Transform and Levelset

Pi¹,

Wang²

2015

Proceedings of the 2015 International Conference on Materials Engineering and Information Technology Applications

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In this paper, we propose a full-automatic algorithm for extracting elongated object from CT image. Given an input image, we use the SWT(Stroke Width Transform) and MM(Mathematical Morphology) to locate the coarse location of target. Thus leading to over-segmentation. In order to refine the segmentation result, Levelset method is applied to make the contour fit the object. Experimental results show that the proposed algorithm gives precise contour in elongated object segmentation.

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Extraction of arbitrary text in natural scene image based on stroke width transform

Cited by 7 publications

References 19 publications

Text Region Identification in Indian Street Scene Images Using Stroke Width Transform and Support Vector Machine

Text Region Identification in Indian Street Scene Images Using Stroke Width Transform and Support Vector Machine

Recent Advancements in Text Detection Methods from Natural Scene Images

Automatic elongated object segmentation in CT image using Stroke Width Transform and Levelset

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