Excited with the new LPRnet model card which would be very useful for real world applications. However, it is stated that the model files are trained on US and Chinease LPs which consists of single line of text. As per the documentation, “the LPRNet model produces a sequence of class IDs. The image feature is divided into slices along the horizontal dimension and each slice is assigned a character ID in the prediction.”
I wonder if the LPRnet is trainable to read plates of countries which contain two lines of texts, for example -
As those plates contains two different lines of text, the image feature should be divided into slices in both horizontal and vertical dimensions and each slice should be assigned a character ID in the prediction. Could anyone enlighten me if it would be sane to retrain the LPRnet for such case? IF so, what type of stride should be in the Resnet architecture?