Databases and tabular data are among the most common and rapidly growing resources. But many of these are poorly annotated (lack sufficient metadata), and are filled with domain specific jargon and alpha-numeric codes. Because of the domain specific jargon, no pre-trained language model could be applied readily to encode the cell content. We propose a deep learning based framework, TableCNN, that encodes the semantics of the surrounding cells to predict the meaning of the columns. We propose application of Byte Pair Encoding (BPE) to create tokens for each cell and treat each cell as a phrase of existing tokens. Once tokenized, we process it with a CNN network to develop a classifier.