Multilingual Artificial Text Extraction and Script Identification from Video Images

Abstract

Abstract-This work presents a system for extraction and script identification of multilingual artificial text appearing in video images. As opposed to most of the existing text extraction systems which target textual occurrences in a particular script or language, we have proposed a generic multilingual text extraction system that relies on a combination of unsupervised and supervised techniques. The unsupervised approach is based on application of image analysis techniques which exploit the contrast, alignment and geometrical properties of text and identify candidate text regions in an image. Potential text regions are then validated by an Artificial Neural Network (ANN) using a set of features computed from Gray Level Co-occurrence Matrices (GLCM). The script of the extracted text is finally identified using texture features based on Local Binary Patterns (LBP). The proposed system was evaluated on video images containing textual occurrences in five different languages including English, Urdu, Hindi, Chinese and Arabic. The promising results of the experimental evaluations validate the effectiveness of the proposed system for text extraction and script identification.

Detection and recognition of cursive text from video frames

“…Script recognition has been studied by researchers for text in video images as well as printed and handwritten documents [50,51]. Recognition of script in video text is naturally much more challenging as opposed to printed or handwritten documents due to low resolution of text and in some cases complex backgrounds [52,53]. From simple methods based on template matching [54] to sophisticated structural [55] and statistical [56] features, a number of techniques have been reported in the literature.…”

Urdu-Text Detection and Recognition in Natural Scene Images Using Deep Learning

“…Much of the current research on Urdu recognition is performed on the cleaned and segmented artificially generated Urdu Nastaliq text such as Urdu Printed Text Images (UPTI) [24], custom extracted [15], generated text with clear background [25], video tickers [26] or handwritten Urdu text [27] as opposed to extracting from outdoor or real-world images with complex background. This work is a step in that direction that integrates synthetic Urdu-text in natural outdoor images.…”

“…While for recognition of Urdu characters from outdoor images there are few custom datasets [11], [15], [25] and for recognition of printed characters words there is a famous dataset UPTI [24], which recently has been updated and has been presented with name UPTI2.0 [38] because the performance on UPTI has reached near saturation [33], [35]. There also exist CLE-18000 [32], [39] which contains near 18K ligatures (compound characters).…”

“…It can be seen that the English Language has the most available datasets [40]- [43] with the text style of a horizontal, oriented, and curved text. Also, English datasets with the multilingual text [7], [8], [15], [44] cover other languages than Urdu text. The number of images in English datasets varies from 500 to more than 60K.…”

“…The number of images in English datasets varies from 500 to more than 60K. Although researchers have mentioned Urdu text datasets [6], [15], [16], [37] for detection and recognition, only one dataset [37] is publicly available for text detection in video frame images. Also, scholars [45], [46] have done bilingual recognition on MSRA-TD [43] and other datasets.…”