%0 Conference Paper %1 doi:10.1117/12.2181370 %A Quehl, Bernhard %A Yang, Haojin %A Sack, Harald %B Proc. SPIE, 9445, 7th International Conference on Machine Vision (ICMV 2014) %D 2015 %K myown ocr visualanalysis %P 944509 %R 10.1117/12.2181370 %T Improving text recognition by distinguishing scene and overlay text %V 9445 %X Video texts are closely related to the content of a video. They provide a valuable source for indexing and interpretation of video data. Text detection and recognition task in images or videos typically distinguished between overlay and scene text. Overlay text is artificially superimposed on the image at the time of editing and scene text is text captured by the recording system. Typically, OCR systems are specialized on one kind of text type. However, in video images both types of text can be found. In this paper, we propose a method to automatically distinguish between overlay and scene text to dynamically control and optimize post processing steps following text detection. Based on a feature combination a Support Vector Machine (SVM) is trained to classify scene and overlay text. We show how this distinction in overlay and scene text improves the word recognition rate. Accuracy of the proposed methods has been evaluated by using publicly available test data sets.

@inproceedings{doi:10.1117/12.2181370, abstract = {Video texts are closely related to the content of a video. They provide a valuable source for indexing and interpretation of video data. Text detection and recognition task in images or videos typically distinguished between overlay and scene text. Overlay text is artificially superimposed on the image at the time of editing and scene text is text captured by the recording system. Typically, OCR systems are specialized on one kind of text type. However, in video images both types of text can be found. In this paper, we propose a method to automatically distinguish between overlay and scene text to dynamically control and optimize post processing steps following text detection. Based on a feature combination a Support Vector Machine (SVM) is trained to classify scene and overlay text. We show how this distinction in overlay and scene text improves the word recognition rate. Accuracy of the proposed methods has been evaluated by using publicly available test data sets. }, added-at = {2015-07-26T15:29:56.000+0200}, author = {Quehl, Bernhard and Yang, Haojin and Sack, Harald}, biburl = {https://www.bibsonomy.org/bibtex/2c6a177dfdefc795aad3c8aea2d700778/lysander07}, booktitle = {Proc. SPIE, 9445, 7th International Conference on Machine Vision (ICMV 2014)}, doi = {10.1117/12.2181370}, interhash = {3f1af6be114af636df58ef6439328756}, intrahash = {c6a177dfdefc795aad3c8aea2d700778}, keywords = {myown ocr visualanalysis}, pages = 944509, timestamp = {2015-07-26T15:29:56.000+0200}, title = {Improving text recognition by distinguishing scene and overlay text }, volume = 9445, year = 2015 }