Robotics has enabled the lessening of human intervention in most of the mission critical applications. For this to happen, the foremost requirement is the identification of objects and their classification. This study aims at building a humanoid robot capable of identifying objects based on the characters on their labels. Traditionally this is facilitated by the analysis of correlation value. However, only relying on this parameter is highly error-prone. This study enhances the efficiency of object identification by using image segmentation and thresholding methods. We have introduced a pre-processing stage for images while subjecting them to correlation coefficient test. It was found that the proposed method gave better recognition rates when compared to the conventional way of testing an image for correlation with another. The obtained results were statistically analysed using the ANOVA test suite. The correlation values with respect to the characters where then fed to the robot to uniquely identify a given image, pick the object using its arm and then place the object in the appropriate container.