  Unconstrained text recognition is a stimulating field in the branch of
pattern recognition. This field is still an open search due to the unlimited
vocabulary, multi styles, mixed-font and their great morphological variability.
Recent trends show a potential improvement of recognition by adoption a novel
representation of extracted features. In the present paper, we propose a novel
feature extraction model by learning a Bag of Features Framework for text
recognition based on Sparse Auto-Encoder. The Hidden Markov Models are then
used for sequences modeling. For features learned quality evaluation, our
proposed system was tested on two printed text datasets PKHATT text line images
and APTI word images benchmark. Our method achieves promising recognition on
both datasets.
