In this paper, a problem of multi-font and multi-size offline printed character recognition of Sindhi language is addressed. Although previous studies for offline handwritten isolated Sindhi character recognition with unique font and size have achieved satisfactory results, the problem of multi-fonts and multi-size character recognition is still a major challenge. This is due to the various varieties in the shape, style, and layout of the character. A synthetic dataset with background color image consisting Sindhi characters with multi-fonts, multi-size, and multi-colors is created. Three types of experiments with Convolutional Neural Networks (CNN) are performed separately. The first CNN network uses max-pooling layer after every two convolutional layers, the second network applies max-pooling layer after the last convolutional layer and the third network is created without applying any max-pooling layer. The experimental results demonstrate that the max-pooling layers used after every two convolutional layers improve the performance significantly. The recognition results of 99.96%, 97.94%, and 98.72% are achieved with first, second and third networks respectively, which shows that CNN outperforms than the traditional machine learning algorithms.