Apply Adaptive Threshold Operation and Conditional Connected-component to Image Text Recognition

摘要

How to effectively extract text from an image is a critical issue in the text recognition domain. Due to the variety of background components, for example, different kind of colors, texture, or brightness in an image will deteriorate the problem of text recognition. In this research, we applied "adaptive threshold operation" and "conditional connected-component" to deal with non-uniform lightness and complicated background images. Different from the general procedure of using the whole image to separate the background from the objects, our research adopted the divide and merge strategy to tackle this problem. Instead of segregating the grayscale image into many regions, our approach partitioned an image into three equal-sized horizontal segments to identify the local threshold value of each segment efficiently. With this approach, we successfully identified and recognized texts from an image. The result shows that the rates of object identification and recognition achieve 81.17% and 91.30%, respectively.