Practising Western Science Outside the West: Personal Observations on the Indian Scene

Modern science, which was an indigenous product of Western culture, is now being practised in many non-Western countries. This paper discusses the peculiar social, cultural and intellectual problems which scientists of these non-Western countries face in adopting Western science in their situations, with special reference to India. It is pointed out that, in addition to money and communication, it is necessary to have a proper psychological gestalt to practise science satisfactorily. The author analyzes his experience as a physics student in India and in the United States to clarify the nature of this psychological gestalt, and to explain what makes it difficult for non-Western scientists to acquire it.

An assessment of the current Indian scene in biotechnology

Biotechnology holds great promise, and is sure to make an impact in India in the nineties. The role of the government's Department of Biotechnology in focusing attention and resources on crucial problems and in supporting basic research is laudable.

Multi-script and multi-oriented text localization from scene images

This paper describes a new method of color text localization from generic scene images containing text of different scripts and with arbitrary orientations. A representative set of colors is first identified using the edge information to initiate an unsupervised clustering algorithm. Text components are identified from each color layer using a combination of a support vector machine and a neural network classifier trained on a set of low-level features derived from the geometric, boundary, stroke and gradient information. Experiments on camera-captured images that contain variable fonts, size, color, irregular layout, non-uniform illumination and multiple scripts illustrate the robustness of the method. The proposed method yields precision and recall of 0.8 and 0.86 respectively on a database of 100 images. The method is also compared with others in the literature using the ICDAR 2003 robust reading competition dataset.

Recognition of Kannada characters extracted from scene images

In this paper, we describe a method for feature extraction and classification of characters manually isolated from scene or natural images. Characters in a scene image may be affected by low resolution, uneven illumination or occlusion. We propose a novel method to perform binarization on gray scale images by minimizing energy functional. Discrete Cosine Transform and Angular Radial Transform are used to extract the features from characters after normalization for scale and translation. We have evaluated our method on the complete test set of Chars74k dataset for English and Kannada scripts consisting of handwritten and synthesized characters, as well as characters extracted from camera captured images. We utilize only synthesized and handwritten characters from this dataset as training set. Nearest neighbor classification is used in our experiments.

NESP: Nonlinear enhancement and selection of plane for optimal segmentation and recognition of scene word images

In this paper, we report a breakthrough result on the difficult task of segmentation and recognition of coloured text from the word image dataset of ICDAR robust reading competition challenge 2: reading text in scene images. We split the word image into individual colour, gray and lightness planes and enhance the contrast of each of these planes independently by a power-law transform. The discrimination factor of each plane is computed as the maximum between-class variance used in Otsu thresholding. The plane that has maximum discrimination factor is selected for segmentation. The trial version of Omnipage OCR is then used on the binarized words for recognition. Our recognition results on ICDAR 2011 and ICDAR 2003 word datasets are compared with those reported in the literature. As baseline, the images binarized by simple global and local thresholding techniques were also recognized. The word recognition rate obtained by our non-linear enhancement and selection of plance method is 72.8% and 66.2% for ICDAR 2011 and 2003 word datasets, respectively. We have created ground-truth for each image at the pixel level to benchmark these datasets using a toolkit developed by us. The recognition rate of benchmarked images is 86.7% and 83.9% for ICDAR 2011 and 2003 datasets, respectively.

Deep searches for decametre-wavelength pulsed emission from radio-quiet gamma-ray pulsars

We report the results of extensive follow-up observations of the gamma-ray pulsar J1732-3131, which has recently been detected at decametre wavelengths, and the results of deep searches for the counterparts of nine other radio-quiet gamma-ray pulsars at 34 MHz, using the Gauribidanur radio telescope. No periodic signal from J1732-3131 could be detected above a detection threshold of 8 sigma, even with an effective integration time of more than 40 h. However, the average profile obtained by combining data from several epochs, at a dispersion measure of 15.44 pc cm(-3), is found to be consistent with that from the earlier detection of this pulsar at a confidence level of 99.2 per cent. We present this consistency between the two profiles as evidence that J1732-3131 is a faint radio pulsar with an average flux density of 200-400 mJy at 34 MHz. Despite the extremely bright sky background at such low frequencies, the detection sensitivity of our deep searches is generally comparable to that of higher frequency searches for these pulsars, when scaled using reasonable assumptions about the underlying pulsar spectrum. We provide details of our deep searches, and put stringent upper limits on the decametre-wavelength flux densities of several radio-quiet gamma-ray pulsars.