Recognition of Bengali Handwritten Characters Using Skeletal Convexity and Dynamic Programming
19 Feb 2011-pp 265-268
TL;DR: This paper presents a novel handwritten character recognition method based on the structural shape of a character irrespective of the viewing direction on the 2D plane, and preliminary results demonstrate the efficacy of this approach.
Abstract: The main challenge in recognizing handwritten characters is to handle large-scale shape variations in the handwriting of different individuals. In this paper, we present a novel handwritten character recognition method based on the structural shape of a character irrespective of the viewing direction on the 2D plane. Structural shape of a character is described by different skeletal convexities of character strokes. Such skeletal convexity acts as an invariant feature for character recognition. Longest common subsequence matching is used for recognition. We have tested out method on a benchmark dataset of handwritten Bengali character images. Preliminary results demonstrate the efficacy of our approach.
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Journal Article•
TL;DR: A literature review on stemmer, optical character recognition (OCR) and text mining work on Indian scripts, mainly on the Gujarati languages is presented in this article, where different techniques for OCR and text extraction in Gujarati scripts are discussed.
Abstract: A lot of work has been reported on optical character recognition for various non-Indian scripts like Chinese, English and Japanese and Indian scripts like Tamil, Hindi Telugu, etc., in this paper, we present a literature review on stemmer, optical character recognition (OCR) and Text mining work on Indian scripts, mainly on the Gujarati languages. We have discussed the different techniques for OCR and text mining in Gujarati scripts, and summarized most of the published work on this topic and gives future directions of research in the field of Indian script.
4 citations
30 Jul 2020
TL;DR: This paper uses 15,000 instances of Bengali alphabets to create a recognition model, which when provided with images of physical pieces of handwritten texts, is able to segment and extract characters from the said image of a physical handwritten text with 65% accuracy and recognize the properly segmented alphABets with 99.5% accuracy.
Abstract: Recognition of Handwritten Character had been one of the promising area of research for its applications in diverse field, it appear to be a challenging research In our paper, we focus specifically on offline handwritten character recognition of regional language (Bengali) by first detecting individual characters The principal approaches for offline handwritten character recognition may be divided into two classes, segmentation and holistic based In our method we applied segmentation based handwritten word recognition and to identify individual characters neural network have been used We have used 15,000 instances of Bengali alphabets to create a recognition model, which when provided with images of physical pieces of handwritten texts, it is able to segment and extract characters from the said image of a physical handwritten text with 65% accuracy, and recognize the properly segmented alphabets with 995% accuracy
4 citations
19 Dec 2020
TL;DR: In this article, a modified ResNet-50 model was used for Bengali compound handwritten characters recognition and achieved an overall accuracy of 96.15% which outperformed the previous best result of 90.33% by a remarkable margin.
Abstract: Optical character recognition (OCR) has been an area of interest for researchers for decades. Many researchers have contributed largely to the development of OCR for script-specific recognition. Handwritten characters recognition has been a big part of this field. Although some literatures have received well-established outcomes, others still haven’t experienced remarkable outcomes yet. Despite being the fifth most spoken language of the world by 228 million people, Bengali has not yet received a remarkable contribution to handwritten characters recognition. Some researchers have offered some promising results for basic Bengali handwritten characters recognition but very few researches offered the recognition of Bengali compound handwritten characters. These few researches have applied support vector machine classifier and some deep neural network classifiers for classification but the outcomes were not much satisfactory. In this research, we considered 171 Bengali compound handwritten characters and apply a modified ResNet-50 model for recognition. We achieved an overall accuracy of 96.15% which outperformed the previous best result of 90.33% by a remarkable margin.
4 citations
Dissertation•
01 Sep 2015
TL;DR: The prime thrust has been made to propose features and utilize a classifier to derive a significant recognition accuracy for Odia character recognition and extensive simulations has been carried out along with other existing schemes using the same data set.
Abstract: In this thesis, we propose four different schemes for recognition of handwritten atomic Odia characters which includes forty seven alphabets and ten numerals. Odia is the mother tongue of the state of Odisha in the republic of India. Optical character recognition (OCR) for many languages is quite matured and OCR systems are already available in industry standard but, for the Odia language OCR is still a challenging task. Further, the features described for other languages can’t be directly utilized for Odia character recognition for both printed and handwritten text. Thus, the prime thrust has been made to propose features and utilize a classifier to derive a significant recognition accuracy. Due to the non-availability of a handwritten Odia database for validation of the proposed schemes, we have collected samples from individuals to generate a database of large size through a digital note maker. The database consists of a total samples of 17, 100 (150 × 2 × 57) collected from 150 individuals at two different times for 57 characters. This database has been named Odia handwritten character set version 1.0 (OHCS v1.0) and is made available in http://nitrkl.ac.in/Academic/Academic_Centers/Centre_For_Computer_Vision.aspx for the use of researchers. The first scheme divides the contour of each character into thirty segments. Taking the centroid of the character as base point, three primary features length, angle, and chord-to-arc-ratio are extracted from each segment. Thus, there are 30 feature values for each primary attribute and a total of 90 feature points. A back propagation neural network has been employed for the recognition and performance comparisons are made with competent schemes. The second contribution falls in the line of feature reduction of the primary features derived in the earlier contribution. A fuzzy inference system has been employed to generate an aggregated feature vector of size 30 from 90 feature points which represent the most significant features for each character. For recognition, a six-state hidden Markov model (HMM) is employed for each character and as a consequence we have fifty-seven ergodic HMMs with six-states each. An accuracy of 84.5% has been achieved on our dataset. The third contribution involves selection of evidence which are the most informative local shape contour features. A dedicated distance metric namely, far_count is used in computation of the information gain values for possible segments of different lengths that are extracted from whole shape contour of a character. The segment, with highest information gain value is treated as the evidence and mapped to the corresponding class. An evidence dictionary is developed out of these evidence from all classes of characters and is used for testing purpose. An overall testing accuracy rate of 88% is obtained.
The final contribution deals with the development of a hybrid feature derived from discrete wavelet transform (DWT) and discrete cosine transform (DCT). Experimentally it has been observed that a 3-level DWT decomposition with 72 DCT coefficients from each high-frequency components as features gives a testing accuracy of 86% in a neural classifier. The suggested features are studied in isolation and extensive simulations has been carried out along with other existing schemes using the same data set. Further, to study generalization behavior of proposed schemes, they are applied on English and Bangla handwritten datasets. The performance parameters like recognition rate and misclassification rate are computed and compared. Further, as we progress from one contribution to the other, the proposed scheme is compared with the earlier proposed schemes.
3 citations
Cites background from "Recognition of Bengali Handwritten ..."
...25 Skeletal convexity [61] 77 71 CFNC scheme 88 80....
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TL;DR: A novel scheme for recognition of handwritten numerals for a regional language Odia of the Indian continent is proposed and satisfactory overall accuracy rate of 96.25% is achieved for Odia numerals.
Abstract: In this paper, we propose a novel scheme for recognition of handwritten numerals for a regional language Odia of the Indian continent. Additional attempts have also been made to implement this scheme for recognition of handwritten numerals of two other languages namely, Bangla and English. Thus, the proposed scheme has been generalised to three different languages. Three variants of time series description of global shapes of numerals have been wrapped up in a vector. This vector is treated as the primary features for the suggested scheme. Satisfactory overall accuracy rate of 96.25% is achieved for Odia numerals. Promising results are also obtained for recognising English and Bangla numerals.
2 citations
Cites background from "Recognition of Bengali Handwritten ..."
..., 2009), Zernike moments (Khotanzad and Hong, 1990), and skeletal convexity (Bag et al., 2011)....
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...These features have been selected from a handful of choice like curvature (Gatos et al., 1997; Pal et al., 2007), f-ratio (Wakabayashi et al., 2009), Zernike moments (Khotanzad and Hong, 1990), and skeletal convexity (Bag et al., 2011)....
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References
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Book•
01 Jan 1990TL;DR: The updated new edition of the classic Introduction to Algorithms is intended primarily for use in undergraduate or graduate courses in algorithms or data structures and presents a rich variety of algorithms and covers them in considerable depth while making their design and analysis accessible to all levels of readers.
Abstract: From the Publisher:
The updated new edition of the classic Introduction to Algorithms is intended primarily for use in undergraduate or graduate courses in algorithms or data structures. Like the first edition,this text can also be used for self-study by technical professionals since it discusses engineering issues in algorithm design as well as the mathematical aspects.
In its new edition,Introduction to Algorithms continues to provide a comprehensive introduction to the modern study of algorithms. The revision has been updated to reflect changes in the years since the book's original publication. New chapters on the role of algorithms in computing and on probabilistic analysis and randomized algorithms have been included. Sections throughout the book have been rewritten for increased clarity,and material has been added wherever a fuller explanation has seemed useful or new information warrants expanded coverage.
As in the classic first edition,this new edition of Introduction to Algorithms presents a rich variety of algorithms and covers them in considerable depth while making their design and analysis accessible to all levels of readers. Further,the algorithms are presented in pseudocode to make the book easily accessible to students from all programming language backgrounds.
Each chapter presents an algorithm,a design technique,an application area,or a related topic. The chapters are not dependent on one another,so the instructor can organize his or her use of the book in the way that best suits the course's needs. Additionally,the new edition offers a 25% increase over the first edition in the number of problems,giving the book 155 problems and over 900 exercises thatreinforcethe concepts the students are learning.
21,651 citations
TL;DR: In this article, a language similar to logo is used to draw geometric pictures using this language and programs are developed to draw geometrical pictures using it, which is similar to the one we use in this paper.
Abstract: The primary purpose of a programming language is to assist the programmer in the practice of her art. Each language is either designed for a class of problems or supports a different style of programming. In other words, a programming language turns the computer into a ‘virtual machine’ whose features and capabilities are unlimited. In this article, we illustrate these aspects through a language similar tologo. Programs are developed to draw geometric pictures using this language.
5,749 citations
TL;DR: A comprehensive survey of thinning methodologies, including iterative deletion of pixels and nonpixel-based methods, is presented and the relationships among them are explored.
Abstract: A comprehensive survey of thinning methodologies is presented. A wide range of thinning algorithms, including iterative deletion of pixels and nonpixel-based methods, is covered. Skeletonization algorithms based on medial axis and other distance transforms are not considered. An overview of the iterative thinning process and the pixel-deletion criteria needed to preserve the connectivity of the image pattern is given first. Thinning algorithms are then considered in terms of these criteria and their modes of operation. Nonpixel-based methods that usually produce a center line of the pattern directly in one pass without examining all the individual pixels are discussed. The algorithms are considered in great detail and scope, and the relationships among them are explored. >
1,827 citations
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
"Recognition of Bengali Handwritten ..." refers methods in this paper
...To improve the recognition performance, many feature selection and extraction methods are reported for Indian languages [1]....
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