Aiming at the problems of casual writing of the offline handwritten text, difficulty in character segmentation, and the dependence of recognition accuracy on a dictionary, an offline handwritten text recognition algorithm based on connectionist temporal classification (CTC)-attention is proposed. The convolutional neural network and bidirectional long short-term memory are used to encode the image features. Multitask learning framework based on CTC and Attention-based models is used to decode feature sequences. In the training process, the CTC model and the attention mechanism model are used to train at the same time, which effectively solves the problem of ignoring the overall information when CTC predicts local information, and the problem of unconstrained decoding of the attention mechanism.Experiments on IAM dataset, i.e., the classical handwritten English word dataset, showed that the character accuracy rate of the proposed method is 93.4%, and the word accuracy rate is 81.8%, proving the proposed method’s feasibility.