Ujjwal Bhattacharya

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

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.

Efficient training and improved performance of multilayer perceptron in pattern classification

Abstract In pattern recognition problems, the convergence of backpropagation training algorithm of a multilayer perceptron is slow if the concerned classes have complex decision boundary. To improve the performance, we propose a technique, which at first cleverly picks up samples near the decision boundary without actually knowing the position of decision boundary. To choose the training samples, a larger set of data with known class label is considered. For each datum, its k-neighbours are found. If the datum is near the decision boundary, then all of these k-neighbours would not come from the same class. A training set, generated using this idea, results in quick and better convergence of the training algorithm. To get more symmetric neighbours, the nearest centroid neighbourhood (Chaudhuri, Pattern Recognition Lett. 17 (1996) 11–17) is used. The performance of the technique has been tested on synthetic data as well as speech vowel data in two Indian languages.

Databases for research on recognition of handwritten characters of Indian scripts

Three image databases of handwritten isolated numerals of three different Indian scripts namely Devnagari, Bangla and Oriya are described in this paper. Grayscale images of 22556 Devnagari numerals written by 1049 persons, 12938 Bangla numerals written by 556 persons and 5970 Oriya numerals written by 356 persons form the respective databases. These images were scanned from three different kinds of handwritten documents - postal mails, job application form and another set of forms specially designed by the collectors for the purpose. The only restriction imposed on the writers is to write each numeral within a rectangular box. These databases are free from the limitations that they are neither developed in laboratory environments nor they are non-uniformly distributed over different classes. Also, for comparison purposes, each database has been properly divided into respective training and test sets.

Online handwritten Bangla character recognition using HMM

We describe here a novel scheme for recognition of online handwritten basic characters of Bangla, an Indian script used by more than 200 million people. There are 50 basic characters in Bangla and we have used a database of 24,500 online handwritten isolated character samples written by 70 persons. Samples in this database are composed of one or more strokes and we have collected all the strokes obtained from the training samples of the 50 character classes. These strokes are manually grouped into 54 classes based on the shape similarity of the graphemes that constitute the ideal character shapes. Strokes are recognized by using hidden Markov models (HMM). One HMM is constructed for each stroke class. A second stage of classification is used for recognition of characters using stroke classification results along with 50 look-up-tables (for 50 character classes).

On recognition of handwritten bangla characters

Recently, a few works on recognition of handwritten Bangla characters have been reported in the literature. However, there is scope for further research in this area. In the present article, results of our recent study on recognition of handwritten Bangla basic characters will be reported. This is a 50 class problem since the alphabet of Bangla has 50 basic characters. In this study, features are obtained by computing local chain code histograms of input character shape. Comparative recognition results are obtained between computation of the above feature based on the contour and one-pixel skeletal representations of the input character image. Also, the classification results are obtained after down sampling the histogram feature by applying Gaussian filter in both these cases. Multilayer perceptrons (MLP) trained by backpropagation (BP) algorithm are used as classifiers in the present study. Near exhaustive studies are done for selection of its hidden layer size. An analysis of the misclassified samples shows an interesting error pattern and this has been used for further improvement in the recognition results. Final recognition accuracies on the training and the test sets are respectively 94.65% and 92.14%.

Direction Code Based Features for Recognition of Online Handwritten Characters of Bangla

In the present article, we describe a novel direction code based feature extraction approach for recognition of online Bangla handwritten basic characters. We have implemented the proposed approach on a database of 7043 online handwritten Bangla (a major script of the Indian subcontinent) character samples, which has been developed by us. This is a 50-class recognition problem and we achieved 93.90% and 83.61% recognition accuracies respectively on its training and test sets.

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

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.

Devanagari and Bangla Text Extraction from Natural Scene Images

With the increasing popularity of digital cameras attached with various handheld devices, many new computational challenges have gained significance. One such problem is extraction of texts from natural scene images captured by such devices. The extracted text can be sent to OCR or to a text-to-speech engine for recognition. In this article, we propose a novel and effective scheme based on analysis of connected components for extraction of Devanagari and Bangla texts from camera captured scene images. A common unique feature of these two scripts is the presence of headline and the proposed scheme uses mathematical morphology operations for their extraction. Additionally, we consider a few criteria for robust filtering of text components from such scene images. Moreover, we studied the problem of binarization of such scene images and observed that there are situations when repeated binarization by a well-known global thresholding approach is effective. We tested our algorithm on a repository of 100 scene images containing texts of Devanagari and / or Bangla.

A majority voting scheme for multiresolution recognition of handprinted numerals

This paper proposes a simple voting scheme for off-line recognition of handprinted numerals. One of the main features of the proposed scheme is that this is not script dependent. Another interesting feature is that it is sufficiently fast for real-life applications. In contrast to the usual practices, here we studied the efficiency of a majority voting approach when all the classifiers involved are multilayer perceptrons (MLP) of different sizes and respective features are based on wavelet transforms at different resolution levels. The rationale for this approach is to explore how one can improve the recognition performance without adding much to the requirements for computational time and resources. For simplicity and efficiency, in the present work, we considered only three coarse-to-fine resolution levels of wavelet representation. We primarily simulated the proposed technique on a database of off-line handprinted Bangla (a major Indian script) numerals. We achieved 97.16% correct recognition rate on a test set of 5000 Bangla numerals. In this simulation we used two other disjoint sets (one for training and the other for validation purpose) of sizes 6000 and 1000 respectively. We have also tested our approach on MNIST database for handwritten English digits. The result is comparable with state-of-the-art technologies.

Online Bangla Word Recognition Using Sub-Stroke Level Features and Hidden Markov Models

For automatic recognition of Bangla script, only a few studies are reported in the literature, which is in contrast to the role of Bangla as one of the worlds major scripts. In this paper we present a new approach to online Bangla handwriting recognition and one of the first to consider cursively written words instead of isolated characters. Our method uses a sub-stroke level feature representation of the script and a writing model based on hidden Markov models. As for the latter an appropriate internal structure is crucial, we investigate different approaches to defining model structures for a highly compositional script like Bangla. In experimental evaluations of a writer independent Bangla word recognition task we show that the use of context-dependent sub-word units achieves quite promising results and significantly outperforms alternatively structured models.