Given an image of a handwritten word, a CNN is employed to estimate its n-gram frequency
profile, which is the set of n-grams contained in the word. Frequencies for unigrams, bigrams …

Training deep learning based handwritten text recognition systems needs a lot of data in
terms of text images and their corresponding annotations. One way to deal with this issue is …

Offline Persian handwriting recognition is a challenging task due to the cursive nature of the
Persian scripts and similarity among the Persian alphabet letters. This paper presents a …

In this paper, we present a model CNN based HMM for Arabic handwriting word recognition.
The HMM have proved a powerful to model the dynamics of handwriting. Meanwhile, the …

In this paper, we present multi-font printed Arabic text recognition using hidden Markov
models (HMMs). We propose a novel approach to the sliding window technique for feature …

In this paper, we present a multi-stage HMM-based text recognition system for handwritten
Arabic. This system employs a novel way of representing Arabic characters by separating …

In this paper, we present an Arabic handwriting synthesis system. Two concatenation
models to synthesize Arabic words from segmented characters are adopted: Extended …

This paper presents a system for offline recognition of cursive Arabic handwritten text based
on Hidden Markov Models (HMMs). The proposed work reports an effective method taking …

In a method of converting an input image patch to a text output, a convolutional neural
network (CNN) is applied to the input image patch to estimate an n-gram frequency profile of …

The challenges that face the handwritten Arabic recognition are overwhelming such as
different varieties of handwriting and few public databases available. Also, teaching the non …