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 …

Handwriting is used to distribute information among people. To access this information for
further analysis the page needs to be optically scanned and converted to machine …

Recognition of cursive handwritten Arabic text is a difficult problem because of context-
sensitive character shapes, the non-uniform spacing between words and within a word …

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 recent years, Deep Learning models have become indispensable in several fields such
as computer vision, automatic object recognition, and automatic natural language …

This paper describes the second edition of the Handwritten Text Recognition (HTR) contest
on the tranScriptorium datasets that has been held in the context of the International …

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 …

This paper presents an offline handwriting recognition system for Bangla script using
sequential detection of characters and diacritics with a Faster R-CNN. This is an entirely …

This work presents state-of-the-art results on one of the complex datasets; known as KHATT.
The KHATT dataset shows complex patterns for Arabic handwritten text. We have achieved …

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 …