Pintu Chandra Shill

Work place: Dept. of Computer Science and Engineering Khulna University of Engineering & Technology Khulna, Bangladesh



Research Interests: Neural Networks, Evolutionary Computation, Artificial Intelligence, Bioinformatics


Pintu Chandra Shill received the B.Sc. degree in Computer Science Engineering (CSE) from Khulna University of Engineering and Technology (KUET), Bangladesh in 2003, the M.Sc. degree in Computer Engineering from Politecnico di Milano, Italy in 2008 and the Doctoral degree in System Design Engineering in 2009 from University of Fukui, Japan. He joined as a lecturer at the Department of CSE, KUET in 2004 and currently he is serving as an Assistant Professor. He has published several research papers in some reputed Journal and Conference. His research interest includes evolutionary computation, fuzzy logic, bioinformatics and artificial neural networks.

Author Articles
Acoustic Modeling of Bangla Words using Deep Belief Network

By Mahtab Ahmed Pintu Chandra Shill Kaidul Islam M. A. H. Akhand

DOI:, Pub. Date: 8 Sep. 2015

Recently, speech recognition (SR) has drawn a great attraction to the research community due to its importance in human-computer interaction bearing scopes in many important tasks. In a SR system, acoustic modelling (AM) is crucial one which contains statistical representation of every distinct sound that makes up the word. A number of prominent SR methods are available for English and Russian languages with Deep Belief Network (DBN) and other techniques with respect to other major languages such as Bangla. This paper investigates acoustic modeling of Bangla words using DBN combined with HMM for Bangla SR. In this study, Mel Frequency Cepstral Coefficients (MFCCs) is used to accurately represent the shape of the vocal tract that manifests itself in the envelope of the short time power spectrum. Then DBN is trained with these feature vectors to calculate each of the phoneme states. Later on enhanced gradient is used to slightly adjust the model parameters to make it more accurate. In addition, performance on training RBMs improved by using adaptive learning, weight decay and momentum factor. Total 840 utterances (20 utterances for each of 42 speakers) of the words are used in this study. The proposed method is shown satisfactory recognition accuracy and outperformed other prominent existing methods.

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Bangla Handwritten Character Recognition using Convolutional Neural Network

By Md. Mahbubar Rahman M. A. H. Akhand Shahidul Islam Pintu Chandra Shill M. M. Hafizur Rahman

DOI:, Pub. Date: 8 Jul. 2015

Handwritten character recognition complexity varies among different languages due to distinct shapes, strokes and number of characters. Numerous works in handwritten character recognition are available for English with respect to other major languages such as Bangla. Existing methods use distinct feature extraction techniques and various classification tools in their recognition schemes. Recently, Convolutional Neural Network (CNN) is found efficient for English handwritten character recognition. In this paper, a CNN based Bangla handwritten character recognition is investigated. The proposed method normalizes the written character images and then employ CNN to classify individual characters. It does not employ any feature extraction method like other related works. 20000 handwritten characters with different shapes and variations are used in this study. The proposed method is shown satisfactory recognition accuracy and outperformed some other prominent exiting methods.

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