To effectively distinguish the handwritten and printed texts, a discrimination method is proposed based on the hidden layer frame features of a convolutional neural network. The hidden layer frame feature is extracted by the convolutional neural network. The Gaussian mixture model is first combined with the hidden Markov model to model the features, and then the Viterbi decoding algorithm is used to determine the category of each frame feature. Based on the recognition results of the frame features, the recognition results are post-processed in combination with the image information. The final handwritten and printed text areas are determined. For the signature document line images, relative to the baseline, the discrimination accuracy of handwritten and printed texts by the proposed method increases by 10.8% and 27.57%, respectively. The effectiveness of the proposed method is verified with the natural scenes, tables and noisy documents.