Gradient-Angular-Features for Word-wise Video Script Identification

Script identification at the word level is challenging because of complex backgrounds and low resolution of video. The presence of graphics and scene text in video makes the problem more challenging. In this paper, we employ gradient angle segmentation on words from video text lines. This paper presents new Gradient-Angular-Features (GAF) for video script identification, namely, Arabic, Chinese, English, Japanese, Korean and Tamil. This work enables us to select an appropriate OCR when the frame has words of multi-scripts. We employ gradient directional features for segmenting words from video text lines. For each segmented word, we study the gradient information in effective ways to identify text candidates. The skeleton of the text candidates is analyzed to identify Potential Text Candidates (PTC) by filtering out unwanted text candidates. We propose novel GAF for the PTC to study the structure of the components in the form of cursiveness and softness. The histogram operation on the GAF is performed in different ways to obtain discriminative features. The method is evaluated on 760 words of six scripts having low contrast, complex background, different font sizes, etc. in terms of the classification rate and is compared with an existing method to show the effectiveness of the method. We achieve 88.2% average classification rate.

[1]  Shijian Lu,et al.  Identification of scripts and orientations of degraded document images , 2010, Pattern Analysis and Applications.

[2]  Umapada Pal,et al.  SVM Based Scheme for Thai and English Script Identification , 2007 .

[3]  Shijian Lu,et al.  New Spatial-Gradient-Features for Video Script Identification , 2012, 2012 10th IAPR International Workshop on Document Analysis Systems.

[4]  David S. Doermann,et al.  Progress in camera-based document image analysis , 2003, Seventh International Conference on Document Analysis and Recognition, 2003. Proceedings..

[5]  Sridha Sridharan,et al.  Texture for script identification , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[6]  Umapada Pal,et al.  Document seal detection using GHT and character proximity graphs , 2011, Pattern Recognit..

[7]  Chew Lim Tan,et al.  Script identification of camera-based images , 2008, 2008 19th International Conference on Pattern Recognition.

[8]  Shijian Lu,et al.  Script and Language Identification in Noisy and Degraded Document Images , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[9]  Umapada Pal,et al.  Word-Wise Script Identification from Video Frames , 2013, 2013 12th International Conference on Document Analysis and Recognition.

[10]  Miguel Angel Ferrer-Ballester,et al.  LBP Based Line-Wise Script Identification , 2013, 2013 12th International Conference on Document Analysis and Recognition.

[11]  Umapada Pal,et al.  Two-stage Approach for Word-wise Script Identification , 2009, 2009 10th International Conference on Document Analysis and Recognition.

[12]  Chew Lim Tan,et al.  Ieee Transactions on Pattern Analysis and Machine Intelligence, Manuscript Id a Laplacian Approach to Multi-oriented Text Detection in Video , 2022 .

[13]  Umapada Pal,et al.  SVM Based Scheme for Thai and English Script Identification , 2007, Ninth International Conference on Document Analysis and Recognition (ICDAR 2007).

[14]  B. Freisleben,et al.  Script recognition in images with complex backgrounds , 2005, Proceedings of the Fifth IEEE International Symposium on Signal Processing and Information Technology, 2005..

[15]  Shijian Lu,et al.  Video Script Identification Based on Text Lines , 2011, 2011 International Conference on Document Analysis and Recognition.

[16]  Bidyut Baran Chaudhuri,et al.  Composite Script Identification and Orientation Detection for Indian Text Images , 2011, 2011 International Conference on Document Analysis and Recognition.

[17]  David S. Doermann,et al.  Identifying script on word-level with informational confidence , 2005, Eighth International Conference on Document Analysis and Recognition (ICDAR'05).

[18]  Debashis Ghosh,et al.  Script Recognition—A Review , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[19]  Jean-Marc Odobez,et al.  Video text recognition using sequential Monte Carlo and error voting methods , 2005, Pattern Recognit. Lett..

[20]  A. G. Ramakrishnan,et al.  Word level multi-script identification , 2008, Pattern Recognit. Lett..

[21]  Palaiahnakote Shivakumara,et al.  A New Method for Word Segmentation from Arbitrarily-Oriented Video Text Lines , 2012, 2012 International Conference on Digital Image Computing Techniques and Applications (DICTA).

[22]  Tieniu Tan,et al.  Rotation Invariant Texture Features and Their Use in Automatic Script Identification , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[23]  Jing Zhang,et al.  Extraction of Text Objects in Video Documents: Recent Progress , 2008, 2008 The Eighth IAPR International Workshop on Document Analysis Systems.