A character recognition method based on an improved convolutional recurrent neural network (CRNN) was proposed for the recognition of characters on the chip surface. The image was binarized by the threshold segmentation based on integral map operation, and the orientation correction of the text field image was completed using affine transformation to achieve the localization of text lines. Based on the original CRNN, the backbone network was replaced with MobileNet-V3 structure and the attention mechanism was added between the two layers of LSTM, while the center loss function was introduced. The improved CRNN was used to implement the text line character recognition and tested on 40 510 chip text line images. The multiple sub-models were obtained by fine-tuning the model training with small sample datasets to achieve integrated inference. The combined recognition accuracy used three models was stable at about 99.97%, and the total recognition time of a single chip image was less than 60 ms. The experimental results showed that the accuracy of the improved CRNN algorithm was improved by about 27.48% over the original CRNN, and the integrated inference of multiple models could achieve higher accuracy.