The automated handwritten digit recognition system has numerous applications. It is required to be performed for address interpretation in postal services, bank cheque processing, or digitization of paper documents. But, for computers to recognize the handwritten numeral images is a challenging task. Various techniques have been utilized for this purpose, like convolutional neural networks architectures. This paper presents a novel design of a Posit-based handwritten digits recognition system, one of the convolutional neural network applications. Posit, a universal number system is a substitute of floating point arithmetic format and is hardware friendly. Herein, LeNet and ResNet-18 based HDRS (Handwritten Digits Recognition System) architecture is used for training and inference of model. The parameters obtained after training were converted to (8,0) Posit number system. Training of LeNet and ResNet-18 based HDRS has been done over the MNIST database, an open-source database for handwritten digits recognition. The proposed Posit (8, 0) based HDRS provides comparable accuracy to traditional floating point and fixed point based HDRS.