Recognition of Handprinted Bangla Numerals Using Neural Network Models

Bhattacharya, Ujjwal; Das, Tanmoy Kanti; Datta, Amitava; Parui, Susanta Kumar; Chaudhuri, B. B.

doi:10.1007/3-540-45631-7_31

Cited by 16 publications

(19 citation statements)

References 10 publications

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“…Multi-Layer Perceptron (MLP) trained by back-propagation (BP) algorithm have been used as classifier. In [18] an automatic recognition scheme for handwritten Bengali numerals using neural network models has been presented.…”

Section: Previous Workmentioning

confidence: 99%

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Bengali Printed Character Recognition – A New Approach

Shaikh

Tabędzki

Chaki

et al. 2013

Computer Information Systems and Industrial Management

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Abstract. This paper presents a new method for Bengali character recognition based on viewbased approach. Both the top-bottom and the lateral view-based approaches have been considered. A layer-based methodology in modification of the basic view-based processing has been proposed. This facilitates handling of unequal logical partitions. The document image is acquired and segmented to extract out the text lines, words, and letters. The whole image of the individual characters is taken as the input to the system. The character image is put into a bounding box and resized whenever necessary. The view-based approach is applied on the resultant image and the characteristic points are extracted from the views after some preprocessing. These points are then used to form a feature vector that represents the given character as a descriptor. The feature vectors have been classified with the aid of k-NN classifier using Dynamic Time Warping (DTW) as a distance measure. A small dataset of some of the compound characters has also been considered for recognition. The promising results obtained so far encourage the authors for further work on handwritten Bengali scripts.

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Section: Previous Workmentioning

confidence: 99%

“…Research is being done on the recognition of both the basic [10] and compound [9] Bengali characters. Attempts have also been made in the recognition of Bengali numerals [13], [18]. The modern Bengali alphabet set consists of 11 vowels and 39 consonants.…”

Section: Introductionmentioning

confidence: 99%

Bengali Printed Character Recognition – A New Approach

Shaikh

Tabędzki

Chaki

et al. 2013

Computer Information Systems and Industrial Management

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“…Chaudhuri and pal [4] Structural and Template Das et al [5] Shadow, Longest run, and Quad-tree Dutta and Chaudhury [6] Structural and Topological Bhattacharya et al [7] [8] Topological and Structural Pal and Chaudhuri [9] Watershed, Topological, and Statistical Bhowmik et al [10] Stroke-based Majumder [11] Curvlet coefficient Table 2. Different feature sets used in Hindi OCR systems.…”

Section: Feature Setmentioning

confidence: 99%

Topographic Feature Extraction for Bengali and Hindi Character Images

Bag¹

2011

SIPIJ

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Feature selection and extraction plays an important role in different classification based problems such as face recognition, signature verification, optical character recognition (OCR) etc. The performance of OCR highly depends on the proper selection and extraction of feature set. In this paper, we present novel features based on the topography of a character as visible from different viewing directions on a 2D plane. By topography of a character we mean the structural features of the strokes and their spatial relations. In this work we develop topographic features of strokes visible with respect to views from different directions (e.g. North, South, East, and West). We consider three types of topographic features: closed region, convexity of strokes, and straight line strokes. These features are represented as a shape-based graph which acts as an invariant feature set for discriminating very similar type characters efficiently. We have tested the proposed method on printed and handwritten Bengali and Hindi character images. Initial results demonstrate the efficacy of our approach

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“…Present input vector P j to the network of n number of nodes; Determine first two nearest nodes to P j using conditions (2) and (3); Modify the weights corresponding to these two nodes using Eqs. (4) and (5); until the change in each weight vector during the last sweep < ε as given by condition (1);…”

Section: Algorithm For Mtasonnmentioning

confidence: 99%

A HYBRID SCHEME FOR HANDPRINTED NUMERAL RECOGNITION BASED ON A SELF-ORGANIZING NETWORK AND MLP ClASSIFIERS

Bhattacharya

Das

Datta

et al. 2002

Int. J. Patt. Recogn. Artif. Intell.

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This paper proposes a novel approach to automatic recognition of handprinted Bangla (an Indian script) numerals. A modified Topology Adaptive Self-Organizing Neural Network is proposed to extract a vector skeleton from a binary numeral image. Simple heuristics are considered to prune artifacts, if any, in such a skeletal shape. Certain topological and structural features like loops, junctions, positions of terminal nodes, etc. are used along with a hierarchical tree classifier to classify handwritten numerals into smaller subgroups. Multilayer perceptron (MLP) networks are then employed to uniquely classify the numerals belonging to each subgroup. The system is trained using a sample data set of 1800 numerals and we have obtained 93.26% correct recognition rate and 1.71% rejection on a separate test set of another 7760 samples. In addition, a validation set consisting of 1440 samples has been used to determine the termination of the training algorithm of the MLP networks. The proposed scheme is sufficiently robust with respect to considerable object noise.

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Recognition of Handprinted Bangla Numerals Using Neural Network Models

Cited by 16 publications

References 10 publications

Bengali Printed Character Recognition – A New Approach

Bengali Printed Character Recognition – A New Approach

Topographic Feature Extraction for Bengali and Hindi Character Images

A HYBRID SCHEME FOR HANDPRINTED NUMERAL RECOGNITION BASED ON A SELF-ORGANIZING NETWORK AND MLP ClASSIFIERS

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