Berat Kurar Barakat

I'm a PhD research student of computer science in Visual Media Lab (VML) at Ben-Gurion University of the Negev.

My latest research is on historical document image analysis using digital image processing and machine learning methods.

Office: 37/-102

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Datasets
VML-MOC: Multiply oriented and curved handwritten text lines dataset is a natural handwritten benchmark dataset for heavily skewed and curved text lines. These text lines are side notes added by scholars over the years on the page margins each time with a different orientation or sometimes in an extremely curvy form due to space constraints. The dataset contains 20 training pages and 10 test pages. Every document image has a corresponding ground truth in the form of pixel labels and PAGE xml.
Challenging text line dataset contains 30 pages from two different manuscripts. It is written in Arabic language and contains 2732 text lines where a considerable amount of them are multidirected, multi-skewed or curved. Ground truth where text lines were labeled manually by line masks, is also available in the dataset.
Complex layout dataset contains 32 document images from 2 manuscripts which were scanned at a private library located at the old city of Jerusalem and other samples which were collected from the Islamic manuscripts digitization project at Leipzig university library.

Publications
Text Line Segmentation for Challenging Handwritten Document Images using Fully Convolutional Network
Berat Kurar Barakat, Ahmad Droby, Majeed Kassis, and Jihad El-Sana
2018 16th International Conference on Frontiers in Handwriting Recognition (ICFHR)
[paper] [poster] [code]
Word Spotting Using Convolutional Siamese Network
Berat Kurar Barakat, Reem Alaasam, and Jihad El-Sana
2018 13th IAPR International Workshop on Document Analysis Systems (DAS)
[paper] [poster] [pitch] [code]
Binarization Free Layout Analysis for Arabic Historical Documents Using Fully Convolutional Networks
Berat Kurar Barakat and Jihad El-Sana
2018 2nd International Workshop on Arabic Script Analysis and Recognition (ASAR)
[paper] [slides] [code]
Case Study Fine Writing Style Classification Using Siamese Neural Network
Alaa Abdalhaleem, Berat Kurar Barakat, and Jihad El-Sana
2018 2nd International Workshop on Arabic Script Analysis and Recognition (ASAR)
[paper] [slides]
Synthesizing versus Augmentation for Arabic Word Recognition with Convolutional Neural Networks
Reem Alaasam, Berat Kurar Barakat, and Jihad El-Sana
2018 2nd International Workshop on Arabic Script Analysis and Recognition (ASAR)
[paper]
Experiment study on utilizing convolutional neural networks to recognize historical Arabic handwritten text
Reem Alaasam, Berat Kurar Barakat, and Jihad El-Sana
2017 1st International Workshop on Arabic Script Analysis and Recognition (ASAR)
[paper]

Honors
ICFHR 2018 Competition on Recognition of Historical Arabic Scientific Manuscripts
Winner Team of Page Segmentation Track
Berat Kurar Barakat, Ahmad Droby, and Jihad El-Sana
[paper] [results] [code]
ASAR 2018 Layout Analysis Competition
Winner Team of Classification Track
Ahmad Droby, Berat Kurar Barakat, and Jihad El-Sana
[paper] [results] [code]

Preprints
Using Scale-Space Anisotropic Smoothing for Text Line Extraction in Historical Documents
Majeed Kassis, Berat Kurar Barakat, Rafi Cohen, Jihad El-Sana, and Klara Kedem
[paper] [code] [metric]