Text provides important information about images and video sequences in a documented image, but it always remains difficult to modify the static documented image. To carry out modification in any of the text matter the text must be segmented out from the documented image, which can be used for further analysis. Taking consideration to video image sequence the isolation of text data from the isolated frame becomes more difficult due to its variable nature.
[...] Workshop on Content-Based Access of Image and Video Database Chung-Wei Liang and Po-Yueh,” DWT based text localization”, Int. J.Appl. Sci. Eng This paper included the extraction of text by using DWT. It realizes an efficient text segmentation algorithm and character recognition for the isolated text data in a documented video image sequence. It is found that the Xian-Sheng Hua., Pei Yin, Hong-Jiang Zhang “Efficient video text recognition using multiple frame recognition”,Microsoft Reasearch Asia,2. C'eline Thillou and Bernard Gosselin“Robust thresholding based on wavelets and thining alogoritm for dgraded camera images”,Facult'e Ploytechnique de Mons, Avanue Corpernic,7000 [...]
[...] These regions are ideally corresponds to image entities such as text blocks. Text in documented vid images can be extracted by extracting the fundamental components using wavelets transform. Thresholding the image into tow levels of the fundamental components result in the uniformity of neighboring pixels and uniforms the intensity flow in a decomposed documented images The binarized images can be operated with morphological analysis and neural network for an efficient extraction of text data from the documented image. Video frames are noisy and low resolution, extraction of such frames is difficult. [...]
[...] In edge-based method, the projection profiles of edge intensity maps can decompose text region and can effectively predict the text data from a given image clip. Text region usually have a special texture because they consist of identical character components. These components contrast the background and have a periodic horizontal intensity variation due to the horizontal alignment of many characters. As a result, text region can be segmented using texture features Document Image Segmentation Document image segmentation is act of partitioning a document image into separated regions. [...]
[...] Wavelet transform is used as an effective tool in isolation of text character from a documented video image. The edges in spatial domain can be found from wavelet transform by identifying peaks at corresponding location. The binarizing of documented image results in the two level isolation of text image, which help in proper extraction of text data from a given documented image. The factor affecting the segmentation result are decomposition level, wavelet function and frequency band. The effectiveness of the proposed system is calculated by percentage of segmentation error defined by Number of misclassified pixels pe= Total number of pixels in the image percentage of error and processing time is less in Spline compare to Haar and Daubechie. [...]
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