crnn

d for recognition: An End-to-End Trainable Neural Network for Image-based Sequence Recognition and Its Application to Scene Text Recognition https://arxiv.org/abs/1507.05717 https://github.com/bgshih/crnn Robust Scene Text Recognition with Automatic Rectification https://arxiv.org/abs/1603.03915

aracters using different fonts, colors etc. 3. Finally the example used in the initial blog post, of predicting 1 vs 0 from images is itself incorrect. Sure a statistician would "train" a linear model to predict 1 vs 0, however I as an ML researcher would NOT train any model at all and would just use [1] which has state of the art performance in character recognition in widely varying conditions. When you have only 80 images, why risk assuming that they are sampled in an IID manner from population, instead why not simply use a model thats trained on far larger population. Now the final argument might look suspicious but its crucial in understanding the difference between AI/ML/CV vs Statistics. In AI/ML/CV the assumption is that there are higher level problems (Character recognition, Object recognition, Scene understanding, Audio recognition) which when solved enable us to apply them in wide variety of situations where they appear. Thus when you encounter a problem like digit recognition the answer an ML researcher would give is to use a state of the art model. [1] https://github.com/bgshih/crnn