Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: In this work, a robust segmentation method for text extraction from the historical document images is presented. The method is based on Markovian-Bayesian clustering on local graphs on both pixel and regional scales. It consists of three steps. In the first step, an over-segmented map of the input image is created. The resulting map provides a rich and accurate semi-mosaic fragments. The map is processed in the second step, similar and adjoining sub-regions are merged together to form accurate text shapes. The output of the second step, which contains accurate shapes, is processed in the final step in which, using clustering with fixed number of classes, the segmentation will be obtained. The method employs significantly the local and spatial correlation and coherence on both the image and beten the stroke parts, and therefore is very robust with respect to the degradation. The resulting segmented text is smooth, and ak connections and loops are preserved thanks to robust nature of the method. The output can be used in succeeding skeletonization processes which require preservation of the text topology for achieving high performance. The method is tested on real degraded document images with promising results.
['Rachid Hedjam', 'Reza Farrahi Moghaddam', 'Mohamed Cheriet']
I am writing a research paper on a idea Text extraction from degraded document images
[{"from": "human", "value": "I am writing a research paper on a idea Text extraction from degraded document images"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: In this work, a robust segmentation method for text extraction from the historical document images is presented. The method is based on Markovian-Bayesian clustering on local graphs on both pixel and regional scales. It consists of three steps. In the first step, an over-segmented map of the input image is created. The resulting map provides a rich and accurate semi-mosaic fragments. The map is processed in the second step, similar and adjoining sub-regions are merged together to form accurate text shapes. The output of the second step, which contains accurate shapes, is processed in the final step in which, using clustering with fixed number of classes, the segmentation will be obtained. The method employs significantly the local and spatial correlation and coherence on both the image and beten the stroke parts, and therefore is very robust with respect to the degradation. The resulting segmented text is smooth, and ak connections and loops are preserved thanks to robust nature of the method. The output can be used in succeeding skeletonization processes which require preservation of the text topology for achieving high performance. The method is tested on real degraded document images with promising results."}]