Multilingual OCR system for South Indian scripts and English documents: An approach based on Fourier transform and principal component analysis

In this paper, a new method for offline handwriting recognition is presented. A robust algorithm for handwriting segmentation has been described here with the help of which individual characters can be segmented from a word selected from a paragraph of handwritten text image which is given as input to the module. Then each of the segmented characters are converted into column vectors of 625 values that are later fed into the advanced neural network setup that has been designed in the form of text files. The networks has been designed with quadruple layered neural network with 625 input and 26 output neurons each corresponding to a character from a-z, the outputs of all the four networks is fed into the genetic algorithm which has been developed using the concepts of correlation, with the help of this the overall network is optimized with the help of genetic algorithm thus providing us with recognized outputs with great efficiency of 71%. character recognition have already been published(II) but the method presented here is advanced than those methods since cursive handwriting can be recognized with the help of a combination of artificial neural networks and genetic algorithm, this becomes the primary advantage of the method over other existing methods. The methodology here has been developed with four multilayer artificial neural networks with Levenberg-Marquardt back propagation algorithm along with genetic algorithm unlike few published methods that use a multilayer feed- forward neural network(III) thus providing an efficient output as compared to the previously published works. The recent spurt in the advancement in handwriting recognition has provided publications one of which discussed here is recognition of text written in 'Oriya', a traditional south-eastern Indian language (IV) but do not involve any combinations of artificial neural networks and optimization techniques such as genetic algorithms which lead to lower efficiency in recognition as compared to the ones that our approach here presents. Several areas have seen application of neural networks such as the Processing of Verbs and Nouns (V), Face Detection (VI) and Real-time Face Detection (VII). The application of genetic algorithms in various areas like initial population generation methods (VIII), mooring pattern optimization (IX), substitution ciphers (X) and designing of reverse logistic networks (XI) has proved its advancement over its predecessors. The handwriting recognition model described here works at three stages, segmentation of the handwritten text, recognition of segmented characters with the help of artificial neural networks and lastly selecting the best solution from the four artificial neural network outputs with the help of genetic algorithm. The cursive handwriting recognition is carried out with the help of artificial neural networks, which is an interconnected group of artificial neurons that uses a mathematical or computational model for information processing based on a connectionistic approach to computation and which has the capability of being adaptive and thus can change its structure based on the information provided to it. The artificial neural networks made in this case contain 625 input neurons, 3 hidden layers and 26 output neurons. The recognition model involves the

http://www.foibg.com/ibs_isc/ibs-02/IBS-02-p03.pdf

Offline handwriting recognition using genetic algorithm

Concerning to optical character recognition, handwriting has sustained to persist as a means of communication and recording information in day to day life even with the introduction of new technologies. Hidden Markov Models (HMM) have long been a popular choice for Western cursive handwriting recognition following their success in speech recognition. However, when it comes to Indic script recognition, the published work employing HMMs is limited, and generally focused on isolated character recognition. A system for offline recognition of cursive handwritten Tamil characters is presented. In this effort, offline cursive handwritten recognition system for Tamil based on HMM and uses a combination of Time domain and frequency domain feature is proposed. The tolerance of the system is evident as it can overwhelm the complexities arise out of font variations and proves to be flexible and robust. Higher degree of accuracy in results has been obtained with the implementation of this approach on a comprehensive database. These initial results are promising and warrant further research in this direction. The results are also encouraging to explore possibilities for adopting the approach to other Indic scripts as well.

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Off-line Cursive Handwritten Tamil Character Recognition

The performance of a character recognition system depends heavily on what features are being used. Though many kinds of features have been developed and their test performances on standard database have been reported, there is still room to improve the recognition rate by developing improved features. In this paper, we propose a novel system based on radon transform for handwritten digit recognition. We have used radon function which represents an image as a collection of projections along various directions. The resultant feature vector by applying this method is the input for the classification stage. A nearest neighbor classifier is used for the subsequent recognition purpose. A test performed on the MNIST handwritten numeral database and on Kannada handwritten numerals demonstrate the effectiveness and feasibility of the proposed method

Robust Unconstrained Handwritten Digit Recognition using Radon Transform

This paper attempts to provide a survey of the past researches on character based as keyword based approaches used for retrieving information from document images. This survey also provides insights into the strengths and weaknesses of current techniques, relevancy lies between each technique and also the guidance in choosing the area that future work on document image retrieval could address.

A survey of keyword spotting techniques for printed document images

Optical Character Recognition (OCR) refers to the process of converting printed Tamil text documents into software translated Unicode Tamil Text. The printed documents available in the form of books, papers, magazines, etc. are scanned using standard scanners which produce an image of the scanned document. As part of the preprocessing phase the image file is checked for skewing. If the image is skewed, it is corrected by a simple rotation technique in the appropriate direction. Then the image is passed through a noise elimination phase and is binarized. The preprocessed image is segmented using an algorithm which decomposes the scanned text into paragraphs using special space detection technique and then the paragraphs into lines using vertical histograms, and lines into words using horizontal histograms, and words into character image glyphs using horizontal histograms. Each image glyph is comprised of 32×32 pixels. Thus a database of character image glyphs is created out of the segmentation phase. Then all the image glyphs are considered for recognition using Unicode mapping. Each image glyph is passed through various routines which extract the features of the glyph. The various features that are considered for classification are the character height, character width, the number of horizontal lines (long and short), the number of vertical lines (long and short), the horizontally oriented curves, the vertically oriented curves, the number of circles, number of slope lines, image centroid and special dots. The glyphs are now set ready for classification based on these features. The extracted features are passed to a Support Vector Machine (SVM) where the characters are classified by Supervised Learning Algorithm. These classes are mapped onto Unicode for recognition. Then the text is reconstructed using Unicode fonts.

Abnikant Singh

Papers

Optical character recognition for printed Tamil text using Unicode