Ayatullah Faruk Mollah

Aliah University

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Co-Authors

Subhadip Basu

Jadavpur University

Ram Sarkar

Jadavpur University

Mita Nasipuri

Jadavpur University

Tauseef Khan

University of Toronto

Neelotpal Chakraborty

Jadavpur University

Nibaran Das

Jadavpur University

Dariusz Plewczyński

University of Warsaw

Payel Sengupta

Aliah University

Emon Asad

Aliah University

Soumi Paul

Dhirubhai Ambani Institute of Information and Communication Technology

Cooperative Institutions

Jadavpur University

Aliah University

University of Warsaw

Warsaw University of Technology

SRM University

VIT-AP University

All India Council for Technical Education

Indian Institute of Engineering Science and Technology, Shibpur

Medical University of Białystok

University of Engineering & Management

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Research Field

Acute Leukemia Subtype Prediction Using EODClassifier

Lecture notes on data engineering and communications technologies (2022)

Salwani Abdullah S. K. Rohit Hasan Ayatullah Faruk Mollah

Blood cancer

10.1007/978-981-16-7610-9_10

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Citations (0)

Convexity Defects-Based Fingertip Detection and Hand Gesture Recognition

Lecture notes in networks and systems (2022)

Soumi Paul Shrouti Gangopadhyay Ayatullah Faruk Mollah Subhadip Basu Mita Nasipuri

Convexity

Numerical digit

10.1007/978-981-19-0105-8_21

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Citations (0)

Hand Signs Recognition from Cellphone Camera Captured Images for Deaf-Mute Persons

Lecture notes in electrical engineering (2021)

Asif Irfanullah Masum Ayatullah Faruk Mollah

10.1007/978-981-16-6448-9_5

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Citations (1)

Line Extraction from Unconstraint Handwritten Document Pages using Piece-wise Water-flow Technique.

Indian International Conference on Artificial Intelligence (2009)

Ram Sarkar Subhadip Basu Nibaran Das Ayatullah Faruk Mollah Mahantapas Kundu

To extract the lines from multi-skewed document images of handwritten Bangla or Roman text, a novel technique is described here. In this paper, at first, the document image is partitioned into number of vertical fragments. Then a hypothetical water-flow technique is applied separately in each vertically fragmented portion of the document images, from both left and right sides of the said partition. After identifying the touching segments, separation of the same is done and finally the water-flow technique is applied on the overall document image. The stripes of areas left unwetted on the image frame are finally labelled for extraction of text lines. To test the developed technique, Bangla and Roman scripts are used and the performance of the technique, as observed experimentally, is 95.54% for the document images of the said scripts.

Document processing

Document image processing

Optical character recognition

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Citations (4)

A two-stage text detection approach using gradient point adjacency and deep network

International Journal of Computational Science and Engineering (2022)

Tauseef Khan Ayatullah Faruk Mollah

Scene text detection is a pivotal problem in computer vision and image processing research. In this paper, a fair attempt is made to design a simple yet effective text detection method in Indic script environment. At first, a fine-scale edge-map is generated from the original image, and subsequently, adaptive clustering is applied to form clusters of edge-points based on their spatial density. Foreground objects are extracted with the help of cluster boundaries and considered as prospective text proposals. Such text proposals are fed to a deep convolutional neural network for learning and prediction as text and non-texts. Finally, true-text components are aggregated as localised final texts of the original image. The proposed method is evaluated on two benchmark datasets viz. ICDAR 2017-MLT and ICDAR 2013 born image, and obtained results are found to surpass some other state-of-the-art methods, which demonstrates its effectiveness in scene and digital environments.

Benchmark (surveying)

Adjacency list

Text Detection

10.1504/ijcse.2022.122210

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Citations (1)

Multiscale phase separation by percolation model with the single chromatin loop resolution

Zenodo (CERN European Organization for Nuclear Research) (2021)

Kaustav Sengupta Michał Denkiewicz Teresa Szczepińska Ayatullah Faruk Mollah Dariusz Plewczyński

GM12878.CTCF.clusters all_edges.csv:- This file contains the set of whole-genome chromatin interactions detected using the ChIA-PET method for CTCF protein that was used in our project. GM12878_subcompartments.csv:- Contains the bins classified into compartments as per Rao et.al. paper. hg19_PET2+_featuretable.tar.gz:- Contains tab-separated files for features sets calculated over loops for each chromosome. anchors_hg19_PET2+_featuretable.tar.gz:- Contains tab-separated files for features sets calculated over anchors for each chromosome.

Percolation (cognitive psychology)

Separation (statistics)

Directed percolation

10.5281/zenodo.4719152

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Hand Segmentation from Complex Background for Gesture Recognition

Advances in intelligent systems and computing (2019)

Soumi Paul Arpan Bhattacharyya Ayatullah Faruk Mollah Subhadip Basu Mita Nasipuri

Identification

Interface (matter)

Joystick

10.1007/978-981-13-7403-6_68

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Citations (20)

Text detection from camera captured images using a novel fuzzy-based technique

Ayatullah Faruk Mollah Subhadip Basu Mita Nasipuri

Text information extraction from camera captured text embedded images has a wide variety of applications. In this paper, a fuzzy membership based robust text detection technique is presented. The given image is partitioned into blocks that are assigned two types of fuzyy memberships. The membership values are post-processed for finer classification as foreground block or background block. Adjacent foreground blocks form foreground components. Then, a feature-based Multi Layer Perceptron is used to classify the foreground components as text or non-text. Experiments show that the number of false negative is very small compared to that of the false positives. The technique yields an average of 99.75% recall and 93.75% precision rates.

Feature (linguistics)

10.1109/eait.2012.6407926

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Citations (10)

Text Detection from Scene and Born Images: How Good is Tesseract?

Algorithms for intelligent systems (2022)

Nadeem Anwar Tauseef Khan Ayatullah Faruk Mollah

Benchmark (surveying)

10.1007/978-981-19-1324-2_13

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Citations (1)

Wheat Head Detection from Outdoor Wheat Field Images Using YOLOv5