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Lalitha Agnihotri - One of the best experts on this subject based on the ideXlab platform.
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Gujarati character recognition
International Conference on Document Analysis and Recognition, 1999Co-Authors: Sameer Antani, Lalitha AgnihotriAbstract:This paper describes the classification of a subset of printed or digitized Gujarati characters. Gujarati belongs to the genre of Devanagri scripts from the Indian subcontinent. Very little work is found in the literature for recognition of Indian language scripts. For this paper a subset of similar appearing Gujarati characters was chosen and subjected to classification by different classifiers. The sample and test images for the characters were obtained from digital images available on the Internet and from scanned images of printed Gujarati text. For their classification, the Euclidean Minimum Distance and the k-Nearest Neighbor classifiers were used with regular and invariant moments. The characters were also classified in the binary feature space using Hamming Distance classifier. The paper presents the recognition rates for these classifiers. A recognition rate of 67% is achieved.
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ICDAR - Gujarati character recognition
Proceedings of the Fifth International Conference on Document Analysis and Recognition. ICDAR '99 (Cat. No.PR00318), 1999Co-Authors: Sameer Antani, Lalitha AgnihotriAbstract:This paper describes the classification of a subset of printed or digitized Gujarati characters. Gujarati belongs to the genre of Devanagri scripts from the Indian subcontinent. Very little work is found in the literature for recognition of Indian language scripts. For this paper a subset of similar appearing Gujarati characters was chosen and subjected to classification by different classifiers. The sample and test images for the characters were obtained from digital images available on the Internet and from scanned images of printed Gujarati text. For their classification, the Euclidean Minimum Distance and the k-Nearest Neighbor classifiers were used with regular and invariant moments. The characters were also classified in the binary feature space using Hamming Distance classifier. The paper presents the recognition rates for these classifiers. A recognition rate of 67% is achieved.
Mukesh M. Goswami - One of the best experts on this subject based on the ideXlab platform.
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Gujarati Text Recognition: A Review
2019 Innovations in Power and Advanced Computing Technologies (i-PACT), 2019Co-Authors: Khushali B. Kathiriya, Mukesh M. GoswamiAbstract:Various commercial OCR systems are available for the western scripts. But there is no sufficient work for Indian scripts including Gujarati script. On the other hand, there are few OCR available for different Indian scripts except for Gujarati script. This paper presents a survey of text recognition techniques for Gujarati script. This survey is classified broadly based on Gujarati script. This paper is the result of efforts in two directions namely printed Gujarati documents, and handwritten Gujarati documents.
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Offline handwritten Gujarati word recognition
2017 Fourth International Conference on Image Information Processing (ICIIP), 2017Co-Authors: Parita R. Paneri, Ronit Narang, Mukesh M. GoswamiAbstract:Gujarati language is an Indo-Aryan language that has a complex structure wherein extracting each character becomes hectic because of the presence of diacritics. Implementation of word recognition technique on the Gujarati database makes the work easy as it does not require extraction of symbols and glyphs from the word image. In this paper, we describe a handwritten Gujarati word recognition technique using Histogram of Oriented Gradients (HoG) features and state of the art classifier like Support Vector Machine (SVM) and k-Nearest Neighbor (kNN). The experiments were performed on a moderate sized database of handwritten Gujarati city names. The work produced has direct application in handwritten postal address processing.
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on line handwritten Gujarati character recognition using low level stroke
IEEE International Conference on Image Information Processing, 2015Co-Authors: Chhaya C Gohel, Mukesh M. Goswami, Vishal K PrajapatiAbstract:This paper presents a low level stroke feature based method for recognition of online handwritten Gujarati characters and numerals. A reasonable size database of online handwritten Gujarati characters and numerals has been developed. This is the first such database of online handwritten symbols for Gujarati script The hierarchical histograms of twelve different low level stroke features and eight directional features were generated to capture the variation in strokes at different level. Recognition is performed using a nearest neighbor (i.e. K-NN) classifier with k-fold cross validation on the dataset having 4500 samples from 45 different classes (37 characters and 8 numerals). Overall Recognition rates achieved are 95%, 93% and 90% for numerals dataset, characters dataset and combine dataset of numerals and characters respectively.
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offline handwritten Gujarati numeral recognition using low level strokes
International Journal of Applied Pattern Recognition, 2015Co-Authors: Mukesh M. Goswami, Suman K MitraAbstract:This paper focuses on the development of offline handwritten Gujarati numeral database of reasonable size and its recognition using low-level stroke features. The database consists of 14,000 samples collected from 140 people with different age group, educational background, and work culture. A novel technique for the extraction of various low-level stroke features, like endpoints, junction points, line segments, and curve segments, is proposed, and the block-wise histogram of low-level stroke features is used for the recognition of offline handwritten numerals from two of the popular Indian scripts, namely Gujarati and Devanagari. The baseline experiments were performed using k-nearest neighbour (k-NN) classifier, and the results were further improved by using the statistically advance support vector machine (SVM) classifier with radial basis function (RBF) kernel. The average test accuracy obtained on Gujarati and Devanagari database were 98.46% and 98.65%, respectively, which is comparable to other existing work. The experiments were also performed on the mixed numerals recognition from Gujarati-Devanagari and Gujarati-English considering the multi-script scenarios in Indian documents.
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structural feature based classification of printed Gujarati characters
Pattern Recognition and Machine Intelligence, 2013Co-Authors: Mukesh M. Goswami, Suman K MitraAbstract:This paper presents a Structural feature based method for classification of printed Gujarati characters. The ability to provide incremental definition of characters in terms of its native components makes the proposal unique and versatile. It deals with varied sizes, font styles, and stoke widths. The features are validated on subset of machine printed Gujarati characters using a simple rule based classifier and the initial results are encouraging.
Rama S Mohan - One of the best experts on this subject based on the ideXlab platform.
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progress in Gujarati document processing and character recognition
2009Co-Authors: Jignesh Dholakia, Atul Negi, Rama S MohanAbstract:Gujarati is an Indic script similar in appearance to other Indo-Aryan scripts. Printed Gujarati script has a rich literary heritage. From an OCR perspective it needs a different treatment due to some of its peculiarities. Research on Gujarati OCR is a recent development as compared to OCR research on many other Indic scripts. Here, in this chapter we present a detailed account of the state of the art of Gujarati document analysis and character recognition. We begin with approaches to zone boundary detection, necessary for the isolation of words and character segmentation and recognition. We show results of various feature extraction techniques such as fringe maps, discrete cosine transform, and wavelets. Zone information and aspect ratios are also used for classification. We present recognition results with two types of classifiers, viz., nearest neighbor classifier and artificial neural networks. Results of experiments wherein various combinations of feature extraction methods with classifiers are also presented. We find that general regression neural network with wavelets feature gives best results with significant time saving in training. Since Indic scripts require syllabic reconstruction from OCR components, a procedure for text generation from the recognized glyph sequences and a method for post-processing is also described.
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zone identification in the printed Gujarati text
International Conference on Document Analysis and Recognition, 2005Co-Authors: J Dholakia, Atul Negi, Rama S MohanAbstract:Gujarati, is a language from the Indo-Aryan family of languages, used by 50 million people in the western part of India. Gujarati-script used to write the Gujarati language, is a multilevel script, written in three zones: base character zone, upper modifier zone and lower modifier zone. Several characters are discriminated by the specific modifiers, which exist in the upper and lower zones. Hence, detecting the zone boundaries is an important task in the Gujarati OCR. Although the Gujarati script is in some respects related to the Devanagari script, there are certain peculiar differences, which prevent the use of already known techniques for zone boundary detection for scripts such as Bengali, Assamese and Devanagari where mature OCR systems already do exist. There is only one previous documented effort for Gujarati OCR, in which an approach to recognize a small subset of Gujarati alphabet was discussed. The present paper proposes a sophisticated method for accurate zone detection in images of printed Gujarati. It is expected that this approach shall make the way smoother for the design and development of Gujarati OCR systems for complete character sets.
Tanish Zaveri - One of the best experts on this subject based on the ideXlab platform.
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Handwritten Gujarati Character Recognition Using Structural Decomposition Technique
Pattern Recognition and Image Analysis, 2019Co-Authors: Ankit Sharma, Dipak M Adhyaru, Priyank Thakkar, Tanish ZaveriAbstract:Handwritten character recognition is the active area of research. Development of Optical Character Recognition (OCR) system for Indian script like Gujarati is still in infancy and hence, there exists many unaddressed challenging problems for research community in this domain. The paper proposes three novel features to represent handwritten Gujarati characters. These features include features extracted based on structural decomposition, zone pattern matching and normalized cross correlation. Methods based on Support Vector Machine (SVM) and Naive Bayes (NB) classifiers have been exercised for the classification of Gujarati characters represented using proposed features. Experiments have been carried out on a dataset of 20500 handwritten Gujarati characters. Experimental results showed significant improvement over state-of-the-art when classifiers were learnt using structural decomposition based features.
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Chain code feature based recognition of handwritten Gujarati numerals
International Journal of Advanced Research in Computer Science, 2017Co-Authors: Ankit Sharma, Dipak M Adhyaru, Tanish ZaveriAbstract:This paper describes chain code based method for handwritten Gujarati numeral recognition. Literature review on Indian OCR indicates that in comparison with Bangla, Hindi, Kannada, Tamil and Telugu scripts, the OCR activities related to Gujarati script is very less. Development of OCR for Gujarati script is quite challenging area for research. In this work, recognition of isolated Gujarati handwritten numerals is performed using chain code based methods. Horizontal scanning and maximum distance from centroid methods are used for deciding the starting point for calculating the chain code sequence. An overall accuracy of 96.37% and 95.62% is obtained using feed forward neural network classifier by the proposed methods respectively. One of the significant contributions of this paper is towards the generation of large and representative database for handwritten Gujarati numerals. Keywords:Chain code, Gujarati handwritten numeral recognition, Neural network classifier.
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Features fusion based approach for handwritten Gujarati character recognition
Nirma University Journal of Engineering and Technology, 2017Co-Authors: Ankit Sharma, Dipak M Adhyaru, Priyank Thakkar, Tanish ZaveriAbstract:Handwritten character recognition is a challenging area of research. Lots of research activities in the area of character recognition are already done for Indian languages such as Hindi, Bangla, Kannada, Tamil and Telugu. Literature review on handwritten character recognition indicates that in comparison with other Indian scripts research activities on Gujarati handwritten character recognition are very less. This paper aims to bring Gujarati character recognition in attention. Recognition of isolated Gujarati handwritten characters is proposed using three different kinds of features and their fusion. Chain code based, zone based and projection profiles based features are utilized as individual features. One of the significant contribution of proposed work is towards the generation of large and representative dataset of 88,000 handwritten Gujarati characters. Experiments are carried out on this developed dataset. Artificial Neural Network (ANN), Support Vector Machine (SVM) and Naive Bayes (NB) classifier based methods are implemented for handwritten Gujarati character recognition. Experimental results show substantial enhancement over state-of-the-art and authenticate our proposals.
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comparative analysis of zoning based methods for Gujarati handwritten numeral recognition
Nirma University International Conference on Engineering, 2015Co-Authors: Ankit Sharma, Dipak M Adhyaru, Tanish Zaveri, Priyank ThakkarAbstract:Gujarati is one of the ancient Indian languages spoken widely by the people of Gujarat state. This paper is concerned with the recognition of handwritten Gujarati numerals. For recognition of Gujarati numerals zoning based Feature extraction method is used. Numeral image is divided in 16×16, 8×8, 4×4 and 2×2 Zones. After feature extraction through the zoning method, Naive Bayes classifier and multilayer feed forward neural network classifier are implemented for the classification of numerals. For the database generation, 14,000 samples of each numeral are used. The overall recognition rates of this method used for recognition of Gujarati numeral using 16×16, 8×8, 4×4 and 2×2 zoning with neural network are 93.03%, 95.92%, 91.89% and 61.78% and with Naive Bayes classifier are 75%, 85.60%, 81% and 53.75% respectively.
Sameer Antani - One of the best experts on this subject based on the ideXlab platform.
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Gujarati character recognition
International Conference on Document Analysis and Recognition, 1999Co-Authors: Sameer Antani, Lalitha AgnihotriAbstract:This paper describes the classification of a subset of printed or digitized Gujarati characters. Gujarati belongs to the genre of Devanagri scripts from the Indian subcontinent. Very little work is found in the literature for recognition of Indian language scripts. For this paper a subset of similar appearing Gujarati characters was chosen and subjected to classification by different classifiers. The sample and test images for the characters were obtained from digital images available on the Internet and from scanned images of printed Gujarati text. For their classification, the Euclidean Minimum Distance and the k-Nearest Neighbor classifiers were used with regular and invariant moments. The characters were also classified in the binary feature space using Hamming Distance classifier. The paper presents the recognition rates for these classifiers. A recognition rate of 67% is achieved.
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ICDAR - Gujarati character recognition
Proceedings of the Fifth International Conference on Document Analysis and Recognition. ICDAR '99 (Cat. No.PR00318), 1999Co-Authors: Sameer Antani, Lalitha AgnihotriAbstract:This paper describes the classification of a subset of printed or digitized Gujarati characters. Gujarati belongs to the genre of Devanagri scripts from the Indian subcontinent. Very little work is found in the literature for recognition of Indian language scripts. For this paper a subset of similar appearing Gujarati characters was chosen and subjected to classification by different classifiers. The sample and test images for the characters were obtained from digital images available on the Internet and from scanned images of printed Gujarati text. For their classification, the Euclidean Minimum Distance and the k-Nearest Neighbor classifiers were used with regular and invariant moments. The characters were also classified in the binary feature space using Hamming Distance classifier. The paper presents the recognition rates for these classifiers. A recognition rate of 67% is achieved.