Bengali alpha-numeric character recognition using curvature features
TL;DR: Curvature properties have been extracted after thinning the smoothed character images and filtering the thinned images using a Gaussian kernel and the unknown samples are classified using a two-stage feed forward neural net based recognition scheme.
About: This article is published in Pattern Recognition.The article was published on 1993-12-01. It has received 98 citations till now. The article focuses on the topics: Curvature & Pattern recognition (psychology).
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TL;DR: A review of the OCR work done on Indian language scripts and the scope of future work and further steps needed for Indian script OCR development is presented.
592 citations
TL;DR: A complete Optical Character Recognition (OCR) system for printed Bangla, the fourth most popular script in the world, is presented and extension of the work to Devnagari, the third most popular Script in the World, is discussed.
381 citations
18 Aug 1997
TL;DR: An OCR system is proposed that can read two Indian language scripts: Bangla and Devnagari (Hindi), the most popular ones in the Indian subcontinent, and shows a good performance for single font scripts printed on clear documents.
Abstract: An OCR system is proposed that can read two Indian language scripts: Bangla and Devnagari (Hindi), the most popular ones in the Indian subcontinent. These scripts, having the same origin in ancient Brahmi script, have many features in common and hence a single system can be modeled to recognize them. In the proposed model, document digitization, skew detection, text line segmentation and zone separation, word and character segmentation, character grouping into basic, modifier and compound character category are done for both scripts by the same set of algorithms. The feature sets and classification tree as well as the knowledge base required for error correction (such as lexicon) differ for Bangla and Devnagari. The system shows a good performance for single font scripts printed on clear documents.
198 citations
TL;DR: A neural network is proposed for Gujarati handwritten digits identification and a multi layered feed forward Neural network is suggested for classification of digits.
176 citations
TL;DR: An extremely fast leaning algorithm called ELM for single hidden layer feed forward networks (SLFN), which randomly chooses the input weights and analytically determines the output weights of SLFN, which learns much faster than traditional popular learning algorithms for feed forward neural networks.
Abstract: This paper deals with the recognition of handwritten Malayalam character using wavelet energy feature (WEF) and extreme learning machine (ELM). The wavelet energy (WE) is a new and robust parameter, and is derived using wavelet transform. It can reduce the influences of different types of noise at different levels. WEF can reflect the WE distribution of characters in several directions at different scales. To a non oscillating pattern, the amplitudes of wavelet coefficients increase when the scale of wavelet decomposition increase. WE of different decomposition levels have different powers to discriminate the character images. These features constitute patterns of handwritten characters for classification. The traditional learning algorithms of the different classifiers are far slower than required. So we have used an extremely fast leaning algorithm called ELM for single hidden layer feed forward networks (SLFN), which randomly chooses the input weights and analytically determines the output weights of SLFN. This algorithm learns much faster than traditional popular learning algorithms for feed forward neural networks. This feature vector, classifier combination gave good recognition accuracy at level 6 of the wavelet decomposition.
175 citations
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01 Jan 1991TL;DR: This book is a detailed, logically-developed treatment that covers the theory and uses of collective computational networks, including associative memory, feed forward networks, and unsupervised learning.
Abstract: From the Publisher:
This book is a comprehensive introduction to the neural network models currently under intensive study for computational applications. It is a detailed, logically-developed treatment that covers the theory and uses of collective computational networks, including associative memory, feed forward networks, and unsupervised learning. It also provides coverage of neural network applications in a variety of problems of both theoretical and practical interest.
7,518 citations
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TL;DR: The state of the art of online handwriting recognition during a period of renewed activity in the field is described, based on an extensive review of the literature, including journal articles, conference proceedings, and patents.
Abstract: This survey describes the state of the art of online handwriting recognition during a period of renewed activity in the field. It is based on an extensive review of the literature, including journal articles, conference proceedings, and patents. Online versus offline recognition, digitizer technology, and handwriting properties and recognition problems are discussed. Shape recognition algorithms, preprocessing and postprocessing techniques, experimental systems, and commercial products are examined. >
922 citations
TL;DR: The problem of finding a description, at varying levels of detail, for planar curves and matching two such descriptions is posed and solved and the result is the ``generalized scale space'' image of a planar curve which is invariant under rotation, uniform scaling and translation of the curve.
Abstract: The problem of finding a description, at varying levels of detail, for planar curves and matching two such descriptions is posed and solved in this paper. A number of necessary criteria are imposed on any candidate solution method. Path-based Gaussian smoothing techniques are applied to the curve to find zeros of curvature at varying levels of detail. The result is the ``generalized scale space'' image of a planar curve which is invariant under rotation, uniform scaling and translation of the curve. These properties make the scale space image suitable for matching. The matching algorithm is a modification of the uniform cost algorithm and finds the lowest cost match of contours in the scale space images. It is argued that this is preferable to matching in a so-called stable scale of the curve because no such scale may exist for a given curve. This technique is applied to register a Landsat satellite image of the Strait of Georgia, B.C. (manually corrected for skew) to a map containing the shorelines of an overlapping area.
894 citations
TL;DR: There still is a great gap between human reading and machine reading capabilities, and a great amount of further effort is required to narrow-down this gap, if not bridge it.
508 citations