Auto-Encoder-BoF/HMM System for Arabic Text Recognition

The recognition of Arabic text, in both handwritten and printed forms, represents a fertile provenance of technical difficulties for Optical Character Recognition (OCR). Indeed, the printed is commonly governed by well-established calligraphy rules and the characters are well aligned. However, there is not always a system capable of reading Arabic printed text in an unconstrained environments such as unlimited vocabulary, multi styles, mixed-font and their great morphological variability. This diversity complicates the choice of features to extract and algorithm of segmentation. In this context, we adopt a new solution for unlimited-vocabulary and mixed-font Arabic printed text recognition. The proposed system is based on the adoption of Bag of Features (BoF) model using Sparse Auto-Encoder (SAE) for features representation and Hidden Markov Models (HMM) for recognition. As results, the obtained average accuracies of recognition vary between 99.65% and 99.96% for the mono-font and exceed 99% for mixed-font.