Offline Handwritten Character & Numeral Recognition A Kernel Based Approach

Automatic Character Recognition for the handwritten Indic script has listed up as most the challenging area for research in the field of pattern recognition. Although a great amount of research work has been reported, but all the state-of-art methods are limited with optimal features. This article aims to suggest a well-defined recognition model which harnessed upon handwritten Odia characters and numerals by implementing a novel process of decomposition in terms of 3rd level Fast Discrete Curvelet Transform (FDCT) to get higher dimension feature vector. After that, Kernel-Principal Component Analysis (K-PCA) considered to obtained optimal features from FDCT feature. Finally, the classification is performed by using Probabilistic Neural Network (PNN) on handwritten Odia character and numeral dataset from both NIT Rourkela and IIT Bhubaneswar. The outcome of proposed scheme outperforms better as compared to existing model with optimized Gaussian kernel-based feature set.

A Hybrid Swarm and Gravitation-based feature selection algorithm for handwritten Indic script classification problem

In any multi-script environment, handwritten script classification is an unavoidable pre-requisite before the document images are fed to their respective Optical Character Recognition (OCR) engines. Over the years, this complex pattern classification problem has been solved by researchers proposing various feature vectors mostly having large dimensions, thereby increasing the computation complexity of the whole classification model. Feature Selection (FS) can serve as an intermediate step to reduce the size of the feature vectors by restricting them only to the essential and relevant features. In the present work, we have addressed this issue by introducing a new FS algorithm, called Hybrid Swarm and Gravitation-based FS (HSGFS). This algorithm has been applied over three feature vectors introduced in the literature recently—Distance-Hough Transform (DHT), Histogram of Oriented Gradients (HOG), and Modified log-Gabor (MLG) filter Transform. Three state-of-the-art classifiers, namely, Multi-Layer Perceptron (MLP), K-Nearest Neighbour (KNN), and Support Vector Machine (SVM), are used to evaluate the optimal subset of features generated by the proposed FS model. Handwritten datasets at block, text line, and word level, consisting of officially recognized 12 Indic scripts, are prepared for experimentation. An average improvement in the range of 2–5% is achieved in the classification accuracy by utilizing only about 75–80% of the original feature vectors on all three datasets. The proposed method also shows better performance when compared to some popularly used FS models. The codes used for implementing HSGFS can be found in the following Github link: https://github.com/Ritam-Guha/HSGFS.

Studies in Pyu phonology, I

The extinct Pyu language was spoken during the first millennium CE and the early centuries of the second millennium CE in what is now Upper Burma. It has been classified as Sino-Tibetan on the basis of basic vocabulary, but its precise position within the family remains unknown. It survives in inscriptions in an Indic script. In this study, the first of its kind, I begin to reconstruct Pyu phonology on the basis of spellings in those inscriptions. I propose that Pyu was a sesquisyllabic language with 7 preinitials and 43 or 44 initials.