Holistic word recognition in handwritten documents is an important research topic in the field of Document Image Analysis. For some applications, given strong language models, it can be more robust and computationally less expensive than character segmentation and recognition. This paper presents HH-CompWordNet, a novel approach to applying a Convolutional Neural Network (CNN) to directly to the DCT coefficients of the compressed domain word images. The efficacy of the HH-CompWordNet is demonstrated with the JPEG compressed version of the CMATERdb2.1.2.1 dataset - standard Handwritten Bangla word images. Experiments show the system obtains state-of-the-art accuracy of 86.80%.