Word-Level Script Identification from Scene Images

Fasil, O. K.; Manjunath, S.; Aradhya, V. N. Manjunath

doi:10.1007/978-981-10-3156-4_43

Cited by 4 publications

(1 citation statement)

References 13 publications

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“…O.K. Fasil et al [14] have proposed Gabor, Log-Gabor and wavelet texture features for Kannada, English and Malayalam script identification at word level from camera captured bus signboards. They performed a series of morphological operations to localize the text from bus signboard image and extracted the 60 texture features from each word.…”

Section: Literature Surveymentioning

confidence: 99%

Camera-Based Bi-lingual Script Identification at Word Level using SFTA Features

Dhandra¹,

Mallappa²,

Mukarambi³

2019

IJRTE

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 Abstract: Most of the documents in various application areas like Government, Business and Research are available in the form of bi-lingual/multi-lingual text document. The multilingual documents are captured from video/camera for identification of script of the text document for automatic reading and editing. In this paper, an attempt is made to address the problem of script identification from camera captured document images using SFTA features. The input image is decomposed into a group of binary images by applying TTBD with fixing the number of the threshold as t n =3 empirically, on each decomposed binary image, Box Count, Mean Gray Level, and Pixel Count are extracted to form the feature vector. This feature vector is submitted to K-NN classifier to identify the scripts of the input document image. In all 10 scripts of the Indian languages are considered along with common English language as bi-lingual documents. The novelty of the paper is that 7 features are selected as potential features to obtain the highest accuracy. Features like Box Count (3), Mean Gray Level(2), and Pixel Count (2) have obtained the 87.02% recognition accuracy for English and Hindi Script combinations for the collected dataset and encouraging results for other combinations. These 7 potential features were selected using the technique named as feed-forward feature selection, from the set all 18 features.

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Section: Literature Surveymentioning

confidence: 99%

Camera-Based Bi-lingual Script Identification at Word Level using SFTA Features

Dhandra¹,

Mallappa²,

Mukarambi³

2019

IJRTE

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show abstract

Scene text understanding: recapitulating the past decade

et al. 2023

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Integrating Local CNN and Global CNN for Script Identification in Natural Scene Images

Huang

et al. 2019

IEEE Access

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Script identification in natural scene images is a key pre-step for text recognition and is also an indispensable condition for automatic text understanding systems that are designed for multilanguage environments. In this paper, we present a novel framework integrating Local CNN and Global CNN both of which are based on ResNet-20 for script identification. We first obtain a lot of patches and segmented images based on the aspect ratios of the images. Subsequently, these patches and segmented images are used as inputs to Local CNN and Global CNN for training, respectively. Finally, to get the final results, the Adaboost algorithm is used to combine the results of Local CNN and Global CNN for decisionlevel fusion. Benefiting from such a strategy, Local CNN fully exploits the local features of the image, effectively revealing subtle differences among the scripts that are difficult to distinguish such as English, Greek, and Russian. Moreover, Global CNN mines the global features of the image to improve the accuracy of script identification. The experimental results demonstrate that our approach has a good performance on four public datasets.

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Word-Level Script Identification from Scene Images

Cited by 4 publications

References 13 publications

Camera-Based Bi-lingual Script Identification at Word Level using SFTA Features

Camera-Based Bi-lingual Script Identification at Word Level using SFTA Features

Scene text understanding: recapitulating the past decade

Integrating Local CNN and Global CNN for Script Identification in Natural Scene Images

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