scispace - formally typeset
Search or ask a question
Journal ArticleDOI

Handwritten Numeral Databases of Indian Scripts and Multistage Recognition of Mixed Numerals

01 Mar 2009-IEEE Transactions on Pattern Analysis and Machine Intelligence (IEEE Computer Society)-Vol. 31, Iss: 3, pp 444-457
TL;DR: P pioneering development of two databases for handwritten numerals of two most popular Indian scripts, a multistage cascaded recognition scheme using wavelet based multiresolution representations and multilayer perceptron classifiers and application for the recognition of mixed handwritten numeral recognition of three Indian scripts Devanagari, Bangla and English.
Abstract: This article primarily concerns the problem of isolated handwritten numeral recognition of major Indian scripts. The principal contributions presented here are (a) pioneering development of two databases for handwritten numerals of two most popular Indian scripts, (b) a multistage cascaded recognition scheme using wavelet based multiresolution representations and multilayer perceptron classifiers and (c) application of (b) for the recognition of mixed handwritten numerals of three Indian scripts Devanagari, Bangla and English. The present databases include respectively 22,556 and 23,392 handwritten isolated numeral samples of Devanagari and Bangla collected from real-life situations and these can be made available free of cost to researchers of other academic Institutions. In the proposed scheme, a numeral is subjected to three multilayer perceptron classifiers corresponding to three coarse-to-fine resolution levels in a cascaded manner. If rejection occurred even at the highest resolution, another multilayer perceptron is used as the final attempt to recognize the input numeral by combining the outputs of three classifiers of the previous stages. This scheme has been extended to the situation when the script of a document is not known a priori or the numerals written on a document belong to different scripts. Handwritten numerals in mixed scripts are frequently found in Indian postal mails and table-form documents.
Citations
More filters
01 Jan 2016
TL;DR: A chronology of key events and quotes from the 12-month investigation into the deaths of six British servicemen and women at the hands of Islamist extremists in Iraq and Syria is revealed.
Abstract: . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiv CHAPTER

3 citations


Cites methods from "Handwritten Numeral Databases of In..."

  • ...written digit datasets, collectively called n-MNIST and three labeled Bangla numeral datasets, created by adding white gaussian noise, adding a motion blur and by reducing the contrast of the original MNIST dataset[105] and the offline handwritten Bangla numeral dataset [16]....

    [...]

  • ...Another set of experiments were performed on the offline Bangla numeral database [16]....

    [...]

  • ...Following the procedure defined in [16], we create a synthetic dataset by using rotation and blurring on the original Bangla dataset....

    [...]

Proceedings ArticleDOI
20 Jan 2022
TL;DR: In this paper , a pre-trained CNN model was used to classify handwritten Gujarati digits from zero to nine using a self-created Gujarati Handwritten Digit Dataset.
Abstract: Digit recognition is a software problem to identify numerals of the specific language using computer system. Digit can be printed or handwritten. Handwritten digit recognition is complex task compare to printed because various writing style, thickness and different curve of handwritten digit are difficult to interpret. Numerous work is performed on the native script of India such as Hindi, Bangla, Gurumukhi, and Tamil. However, research efforts on Gujarati Handwritten digit or character recognition are reported very less. This paper aims to demonstrate the efficiency of transfer learning and utilization of a pre-trained model developed for ImageNet dataset to classify handwritten Gujarati digits from zero to nine. The proposed framework developed from the Convolutional and pooling layers of VGG-16, VGG-19, ResNet50, ResNet101, InceptionV3 and EfficentNet pre-trained CNN networks for the feature extraction and newly defined fully-connected layers and output layer for the classification. The proposed framework is investigated on self-created Gujarati Handwritten Digit Dataset. Experimental results show that EfficientNet achieved highest accuracy (training accuracy − 94.9% and testing accuracy − 94.98%) among six pre-trained networks using proposed framework.

2 citations

Dissertation
26 Jan 2019

2 citations


Cites background from "Handwritten Numeral Databases of In..."

  • ...A multistage recognition scheme for mixed numerals is reported recently [32]....

    [...]

Proceedings ArticleDOI
19 Mar 2015
TL;DR: A learning based handwritten text categorization technique using Malsburg Learning BP Network combination has been designed and developed and used to identify alpha numerals and thereafter convert the handwritten text into printed one via appropriate word formation.
Abstract: In the present work a learning based handwritten text categorization technique using Malsburg Learning BP Network combination has been designed and developed This combination is used to identify alpha numerals and thereafter convert the handwritten text into printed one via appropriate word formation Groups of text belonging to different subjects are fed to this system, and the system extracts the salient features in terms of attributes in intra groups The commonality among the inter groups are thereafter discarded The attributes or salient features thereby learned are later used as glossary for each group for performing unlabeled text categorization The performance evaluation of this system with labeled test texts using standard Holdout Method in terms of accuracy, precision, recall, f-score is appreciable Also the learning and performance evaluation time is affordable

2 citations


Cites background from "Handwritten Numeral Databases of In..."

  • ...Another approach illustrates training alphabet dataset using multilayer perceptron classifiers [10]....

    [...]

References
More filters
Journal ArticleDOI
01 Jan 1998
TL;DR: In this article, a graph transformer network (GTN) is proposed for handwritten character recognition, which can be used to synthesize a complex decision surface that can classify high-dimensional patterns, such as handwritten characters.
Abstract: Multilayer neural networks trained with the back-propagation algorithm constitute the best example of a successful gradient based learning technique. Given an appropriate network architecture, gradient-based learning algorithms can be used to synthesize a complex decision surface that can classify high-dimensional patterns, such as handwritten characters, with minimal preprocessing. This paper reviews various methods applied to handwritten character recognition and compares them on a standard handwritten digit recognition task. Convolutional neural networks, which are specifically designed to deal with the variability of 2D shapes, are shown to outperform all other techniques. Real-life document recognition systems are composed of multiple modules including field extraction, segmentation recognition, and language modeling. A new learning paradigm, called graph transformer networks (GTN), allows such multimodule systems to be trained globally using gradient-based methods so as to minimize an overall performance measure. Two systems for online handwriting recognition are described. Experiments demonstrate the advantage of global training, and the flexibility of graph transformer networks. A graph transformer network for reading a bank cheque is also described. It uses convolutional neural network character recognizers combined with global training techniques to provide record accuracy on business and personal cheques. It is deployed commercially and reads several million cheques per day.

42,067 citations

Book
16 Jul 1998
TL;DR: Thorough, well-organized, and completely up to date, this book examines all the important aspects of this emerging technology, including the learning process, back-propagation learning, radial-basis function networks, self-organizing systems, modular networks, temporal processing and neurodynamics, and VLSI implementation of neural networks.
Abstract: From the Publisher: This book represents the most comprehensive treatment available of neural networks from an engineering perspective. Thorough, well-organized, and completely up to date, it examines all the important aspects of this emerging technology, including the learning process, back-propagation learning, radial-basis function networks, self-organizing systems, modular networks, temporal processing and neurodynamics, and VLSI implementation of neural networks. Written in a concise and fluid manner, by a foremost engineering textbook author, to make the material more accessible, this book is ideal for professional engineers and graduate students entering this exciting field. Computer experiments, problems, worked examples, a bibliography, photographs, and illustrations reinforce key concepts.

29,130 citations

Journal ArticleDOI
TL;DR: In this paper, it is shown that the difference of information between the approximation of a signal at the resolutions 2/sup j+1/ and 2 /sup j/ (where j is an integer) can be extracted by decomposing this signal on a wavelet orthonormal basis of L/sup 2/(R/sup n/), the vector space of measurable, square-integrable n-dimensional functions.
Abstract: Multiresolution representations are effective for analyzing the information content of images. The properties of the operator which approximates a signal at a given resolution were studied. It is shown that the difference of information between the approximation of a signal at the resolutions 2/sup j+1/ and 2/sup j/ (where j is an integer) can be extracted by decomposing this signal on a wavelet orthonormal basis of L/sup 2/(R/sup n/), the vector space of measurable, square-integrable n-dimensional functions. In L/sup 2/(R), a wavelet orthonormal basis is a family of functions which is built by dilating and translating a unique function psi (x). This decomposition defines an orthogonal multiresolution representation called a wavelet representation. It is computed with a pyramidal algorithm based on convolutions with quadrature mirror filters. Wavelet representation lies between the spatial and Fourier domains. For images, the wavelet representation differentiates several spatial orientations. The application of this representation to data compression in image coding, texture discrimination and fractal analysis is discussed. >

20,028 citations

Book ChapterDOI
01 Jan 1988
TL;DR: This chapter contains sections titled: The Problem, The Generalized Delta Rule, Simulation Results, Some Further Generalizations, Conclusion.
Abstract: This chapter contains sections titled: The Problem, The Generalized Delta Rule, Simulation Results, Some Further Generalizations, Conclusion

17,604 citations


Additional excerpts

  • ...Ç...

    [...]