The technology has become an essential part of digitizing documents for banks, educational and others. In this paper we have craft a handwriting to text converter by using CNN which is able to input the handwritten character and convert it into computerized text. This is because CNNs are really good at image processing and what we are doing in the input debugger is identifying and splitting those individual characters from a huge variety of handwritten input. A dataset of handwritten characters is used to train the model, which uses its hierarchical feature extraction capabilities to pick up patterns and subtleties on how handwriting appears. The obtained results from the experiments identifies that CNNs are very good in predicting high accuracy and low error rate for handwriting to text conversion hence using them in real world application makes a boom on it by performing well across industries.