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One of the most recent challenging issues of pattern recognition and artificial intelligence is Arabic text recognition. This research topic is still a pervasive and unaddressed research field, because of several factors. Complications arise due to the cursive nature of the Arabic writing, character similarities, unlimited vocabulary, use of multi-size and mixed-fonts, etc. To handle these challenges, an automatic Arabic text recognition requires building a robust system by computing discriminative features and applying a rigorous classifier together to achieve an improved performance. In this work, we introduce a new deep learning based system that recognizes Arabic text contained in images. We propose a novel hybrid network, combining a Bag-of-Feature (BoF) framework for feature extraction based on a deep Sparse Auto-Encoder (SAE), and Hidden Markov Models (HMMs), for sequence recognition. Our proposed system, termed BoF-deep SAE-HMM, is tested on four datasets, namely the printed Arabic line images Printed KHATT (P-KHATT), the benchmark printed word images Arabic Printed Text Image (APTI), the benchmark handwritten Arabic word images IFN/ENIT, and the benchmark handwritten digits images Modified National Institute of Standards and Technology (MNIST).

Unconstrained handwriting text recognition is a stimulating field in the branch of pattern recognition. This field is still an open search due to the wide variability of human writing. 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 handwritten text recognition based on Deep 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 handwritten text datasets IFN/ENIT word images benchmark and MNIST handwritten digits. Our method achieves promising recognition on both datasets.

The relevant entity extraction from scanned document image is a very challenging task due to highly heterogeneous templates, and several structure layouts. These problems lead to inaccuracy for document image recognized by OCR. In this paper, we propose an effective solution for these problems, in which the relevant entities are extracted from Arabic and Latin scanned invoices. The input of the system is an invoice image which is submitted to an OCR without layout analysis. After, invoices are labeled in the text recognized by the OCR. By combining the logical and physical structures, a local graph model is built for extraction entity. Finally, we implement a correction module which requires the mislabeling correction by eliminating the superfluous parts detected by labeling step. We evaluate the obtained results with 1050 real invoices as reported in experimental section.

The logical and semantic structure analysis is a basic process for invoice understanding. Be able to carry out a robust layout analysis is very difficult due to highly heterogeneous invoice templates. In this paper, we propose a local structure for entity extraction and correction from scanned invoices. It attempts to extract entity in contiguous and noncontiguous structure by automatic finding the local structure of each entity without structure model matching and user intervention. Firstly, the entities are labeled in OCRed invoice. Combining labeled entities with geometric and semantic relations, token structure models are generated. These models are used for entity extraction and mislabeling correction by ignoring some superfluous tokens detected by labeling step. The correction module to the contiguous structure differs from that of the noncontiguous structure. The obtained results with a dataset of real invoices are reported in experimental section.