This page grants access to our generated SVHN datasets and trained models, that are mentioned in our AAAI-18 paper "SEE: Towards Semi-Supervised End-to-End Scene Text Recognition" (preprint available here). The code for this publication is available here.
The page about our previous Arxiv publication "STN-OCR: A single Neural Network for Text Detection and Text Recognition" contains all data necessary for redoing our experiments on the SVHN datasets. You can find the page here. Please note: the models on this page won't work with the code for this paper, but with the code for the other paper. The other paper is the pre-version of this paper.
We've also prepared a video that shows the train progress of our model on the SVHN dataset with randomly placed numbers. You can find the video here.
For training and evaluating on the FSNS dataset we prepared the dataset as described here.
The file fsns_model.zip contains our best performing model on the FSNS dataset. You can use this data with the evaluation.py script in our Github repository to evaluate the model.
For this dataset, we also prepared a video showing the train progress on this dataset. You can find the video here.
Although not mentioned in the paper, we also performed pure text recognition experiments on already extracted text lines (analog to the experiments described in our STN-OCR paper).
The file text_recognition_model.zip contains a text recognition model and also a small example dataset including all necessary files. If you want to use this dataset, you will need to adapt some filepaths!
The file supplementary_material.pdf contains supplementary material for our paper.