An offline recognition system for Arabic handwritten words is presented. The recognition system is based on a semi-continuous 1-dimensional HMM. From each binary word image normalization parameters were estimated. First height, length, and baseline skew are normalized, then features are collected using a sliding window approach. This paper presents these methods in more detail. Some parameters ...

In spite of various applications digit, letter and word recognition, only a few studies have dealt with Persian scripts. this paper, deep neural networks are utilized through different DenseNet Xception architectures, being further boosted by means data augmentation test time augmentation. Dividing the datasets to training, validation sets, utilizing k-fold cross-validation, comparison proposed...

Keyword retrieval in handwritten document images (word spotting) is very challenging given that OCR accuracy is not yet adequate for handwritten scripts, specially with large lexicons. Various proposed approaches build indices on information such as image features or OCR scores and have improved the performance of the traditional approach that builds index on OCR’ed text. In this paper, we impr...

FARSI HANDWRITTEN WORD RECOGNITION USING CONTINUOUS HIDDEN MARKOV MODELS AND STRUCTURAL FEATURES

This paper presents a new technique of high accuracy to recognize both typewritten and handwritten English and Arabic texts without thinning. After segmenting the text into lines (horizontal segmentation) and the lines into words, it separates the word into its letters. Separating a text line (row) into words and a word into letters is performed by using the region growing technique (implicit s...

One of the most important and challenging tasks in a handwritten recognition pipeline is the segmentation of handwritten document images into text lines and words. Several problems inherent in handwritten documents such as the difference in the skew angle between text lines or along the same text line, the existence of adjacent text lines or words touching, the existence of characters with diff...

This paper offers a solution to traditional handwriting recognition techniques using concepts of Deep learning and Word Beam Search. explains about how an individual handwritten word is classified from the text by translating into digital form. The form when trained with Connectionist Temporal Classification (CTC) loss function, output produced RNN. matrix containing character probabilities for...