Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: A binarization method is presented to counter the stroke connectivity problems of characters arising from mid-level-quality binary image scanning systems. In the output of a binary image scanning system, separate strokes may look connected if the point size is small and the character strokes are complex while strokes may lose connectivity if they are generated at low intensity. Also, erroneous recognition may result if a blemished document surface distorts the image. To counter these problems and to further enhance the quality of character recognition, the authors have developed an integrated binarization scheme, exploiting synergistic use of an adaptive thresholding technique and variable histogram equalization. This algorithm is composed of two components. The first removes background noise via gray level histogram equalization while the second enhances the gray level of characters over and above the surrounding background via an edge image composition technique.
['Moon-Soo Chang', 'Sunmee Kang', 'Woo-Sik Rho', 'Heok-Gu Kim', 'Duck-Jin Kim']
I am writing a research paper on a idea Improved binarization algorithm for document image by histogram and edge detection
[{"from": "human", "value": "I am writing a research paper on a idea Improved binarization algorithm for document image by histogram and edge detection"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: A binarization method is presented to counter the stroke connectivity problems of characters arising from mid-level-quality binary image scanning systems. In the output of a binary image scanning system, separate strokes may look connected if the point size is small and the character strokes are complex while strokes may lose connectivity if they are generated at low intensity. Also, erroneous recognition may result if a blemished document surface distorts the image. To counter these problems and to further enhance the quality of character recognition, the authors have developed an integrated binarization scheme, exploiting synergistic use of an adaptive thresholding technique and variable histogram equalization. This algorithm is composed of two components. The first removes background noise via gray level histogram equalization while the second enhances the gray level of characters over and above the surrounding background via an edge image composition technique."}]