Segmentation of text lines and words in an unconstrained handwritten or a machine-printed degraded document is a challenging document analysis problem due to the heterogeneity in the document structure. Often there is un-even skew between the lines and also broken words in a document. In this article, the contribution lies in segmentation of a document page image into lines and words. We have proposed an unsupervised, robust, and simple statistical method to segment a document image that is either handwritten or machine-printed (degraded or otherwise). In our proposed method, the segmentation is treated as a two-class classification problem. The classification is done by considering the distribution of gap size (between lines and between words) in a binary page image. Our method is very simple and easy to implement. Other than the binarization of the input image, no pre-processing is necessary. There is no need of high computational resources. The proposed method is unsupervised in the sense that no annotated document page images are necessary. Thus, the issue of a training database does not arise. In fact, given a document page image, the parameters that are needed for segmentation of text lines and words are learned in an unsupervised manner. We have applied our proposed method on several popular publicly available handwritten and machine-printed datasets (ISIDDI, IAM-Hist, IAM, PBOK) of different Indian and other languages containing different fonts. Several experimental results are presented to show the effectiveness and robustness of our method. We have experimented on ICDAR-2013 handwriting segmentation contest dataset and our method outperforms the winning method. In addition to this, we have suggested a quantitative measure to compute the level of degradation of a document page image.

Deep neural network architectures have been used successfully in various document analysis studies. Its strength in producing human like performance has already been explored in handwritten English numeral recognition task. In this context, a natural question that often arises in a practitioner's mind: does an increase in the depth of the network eventually lead to an improved recognition performance on unknown samples? A goal of the present work is to search for an answer of the same through a case study of a larger class handwriting recognition problem. Here, we have studied recognition of handwritten Devanagari characters. In this study, we have implemented convolutional neural network (CNN) architectures of five different depths. We have also implemented additional neural architectures by adding two Bidirectional Long Short Term Memory (BLSTM) layers between the convolutional stack and the fully connected part of each of these five CNN networks. Simulations have been performed on two different databases of handwritten Devanagari characters consisting of 30408 and 36172 samples and a combined set consisting of 58451 samples. The recognition accuracy obtained in the best case improves significantly the existing state-of-the-art of this handwriting recognition problem. Also, further analysis of our simulation results provides an answer to the above question. Additionally, we have trained a BLSTM network alone using the Histogram of Oriented Gradient (HOG) features. Performance of this architecture failed to compete with the performance of CNN-BLSTM hybrid architecture.

It presents our recent study on segmentation-free recognition of unconstrained cursive online handwriting of Devanagari and Bangla, the two most popular Indic scripts. Here, we have devised an efficient algorithm for obtaining the core region of a handwritten word sample and compute a robust feature set which includes certain measures based on the knowledge of this core region. It also includes first and second order discrete derivatives which estimate certain geometric properties of discrete curves along the pen trajectory of the input online handwritten word sample. The proposed feature vector is obtained at each point on the trajectory of the preprocessed word sample based on a window (called vicinity) centered at the point. A hybrid deep neural network architecture consisting of a convolutional neural network (CNN), a bidirectional recurrent neural network (BiRNN) having Long Short Term Memory (LSTM) cells and a connectionist temporal classification (CTC) layer receives this high level feature vector as input for labelling the character sequence in the online word sample. This study shows that the proposed hybrid architecture recognizes online handwriting more efficiently than a BLSTM network alone.

During the last 20 years, significant research studies have been undertaken for automatic recognition of printed documents. The same is true for Bangla, a major Indian script. All these studies were mainly centered on comparatively well-behaved good quality printed documents. However, many of the large archives include significant volumes of older documents which are so degraded in their present form that they cannot be reasonably transcribed using the existing OCR (Optical Character Recognition) approaches. On the other hand, automatic recognition of printed contents of these documents has significant application potentials such as generation of descriptive metadata, full-text searching, information extraction etc. The contributions made in the present study are (i) creation of a moderately large annotated database of degraded Bangla documents towards their recognition studies, (ii) development of a Gaussian mixture model based strategy for extraction of text components from complex noisy background of such documents and (iii) development of a line level recognition scheme for degraded Bangla documents. We have studied two different CNN-BLSTM-CTC hybrid architectures for this recognition problem. The winning architecture uses the first convolution layer of the CNN in a fashion similar to the inception model of deep learning methodologies.

In this article, a region-based Deep Convolutional Neural Network framework is presented for document structure learning. The contribution of this work involves efficient training of region based classifiers and effective ensembling for document image classification. A primary level of `inter-domain' transfer learning is used by exporting weights from a pre-trained VGG16 architecture on the ImageNet dataset to train a document classifier on whole document images. Exploiting the nature of region based influence modelling, a secondary level of `intra-domain' transfer learning is used for rapid training of deep learning models for image segments. Finally, a stacked generalization based ensembling is utilized for combining the predictions of the base deep neural network models. The proposed method achieves state-of-the-art accuracy of 92.21% on the popular RVL-CDIP document image dataset, exceeding the benchmarks set by the existing algorithms.

Automatic recognition of online handwritten words in a generic mode has significant application potentials. However, this recognition job is challenging for unconstrained handwriting data. The challenge is more serious for Indic scripts like Devanagari or Bangla due to the inherent cursiveness of their characters, large sizes of respective alphabets, existence of several groups of shape similar characters etc. On the other hand, with the recent development of powerful machine learning tools, major research initiatives in this area of pattern recognition studies have been observed. Feature extraction and classification are two major modules of such a recognizer. Deep architectures of convolutional neural network (CNN) models have been found to be efficient in extraction of useful features from raw signal. On the other hand, a recurrent neural network (RNN) along with connectionist temporal classification (CTC) has been shown to be able to label unsegmented sequence data. In the present article, we propose a hybrid layered architecture consisting of three networks CNN, RNN and CTC for recognition of online handwriting without use of any specific lexicon. In this study, we have also observed that feeding hand-crafted features to the CNN at the first level of the proposed model provides better performance than feeding the raw signal to the CNN. We have simulated the proposed model on two large databases of Devanagari and Bangla online unconstrained handwritten words. The recognition accuracies provided by the proposed model are encouraging.

Lasso and sub-sampling based techniques (e. g. Stability Selection) are nowadays most commonly used methods for detecting the set of active predictors in high-dimensional linear models. The consist ...

A few studies of online Bangla handwriting recognition such as isolated character recognition or limited vocabulary cursive word recognition are found in the literature. However, development of an end-to-end recognition system of unconstrained online Bangla handwritten texts has not been duly attempted so far. In the present report, we describe a similar system which takes a piece of continuous online handwritten Bangla texts as the input. It first segments the input texts into individual lines, each line into its constituent words and each word into sub-strokes. In the present study, 152 different symbols which include basic characters, character modifiers, frequently used conjunct characters, a few special characters and numerals have been considered. The entire set of sub-strokes obtained from the training sample set has been exhaustively studied by 3 experts and 76 different shapes of sub-strokes have been identified based on consensus among these experts. Also, it has been observed that a character may produce at most 3 sub-strokes. Since a piece of Bangla texts often contains either Bangla or English numerals, the present character set consists of both the numeral set and 3 numeral shapes are common to both the scripts. The proposed recognition system uses two classifiers, one for characters and the other for sub-strokes. Sub-strokes are fed to the character classifier in their temporal order. A single sub-stroke followed by two consecutive sub-strokes and finally three successive sub-strokes are passed to the character classifier and the first two top responses of the character classifier among the three cases are compared. If the difference is less than a threshold, the response of sub-stroke classifier is used to reach a final decision. The proposed system provided 94.3% character level accuracy on a test set consisting of 33,453 word samples written by 31 writers.

In this article, we propose a new framework for segmentation of Bangla handwritten word images into meaningful individual symbols or pseudo-characters. Existing segmentation algorithms are not usually treated as a classification problem. However, in the present study, the segmentation algorithm is looked upon as a two-class supervised classification problem. The method employs an SVM classifier to select the segmentation points on the word image on the basis of various structural features. For training of the SVM classifier, an unannotated training set is prepared first using candidate segmenting points. The training set is then clustered, and each cluster is labeled manually with minimal manual intervention. A semi-automatic bootstrapping technique is also employed to enlarge the training set from new samples. The overall architecture describes a basic step toward building an annotation system for the segmentation problem, which has not so far been investigated. The experimental results show that our segmentation method is quite efficient in segmenting not only word images but also handwritten texts. As a part of this work, a database of Bangla handwritten word images has also been developed. Considering our data collection method and a statistical analysis of our lexicon set, we claim that the relevant characteristics of an ideal lexicon set are present in our handwritten word image database.

In relevance feedback, first-round search results are used to boost second-round search results. Two forms have been traditionally considered: exhaustively labelled feedback, where all first-round results to depth k are annotated for relevance by the user; and blind feedback, where the top-k results are all assumed to be relevant. In this paper, we consider an intermediate, semi-supervised scheme, in which only a subset of results is selected for annotation, and then their labels are propagated to their nearest neighbours. Specifically, we use clustering to determine the nearest-neighbour groups, and seed selection to choose documents for annotation. We find that the effectiveness of this method is indistinguishable from the exhaustive relevance feedback, and is significantly higher than both blind feedback and the use of the annotated subset alone. We show that this approach works well in environments in which some but limited amounts of human feedback are available, such as early case assessment in e-discovery.

There are many scripts in the world, several of which are used by hundreds of millions of people. Handwritten character recognition studies of several of these scripts are found in the literature. Different hand-crafted feature sets have been used in these recognition studies. However, convolutional neural network (CNN) has recently been used as an efficient unsupervised feature vector extractor. Although such a network can be used as a unified framework for both feature extraction and classification, it is more efficient as a feature extractor than as a classifier. In the present study, we performed certain amount of training of a 5-layer CNN for a moderately large class character recognition problem. We used this CNN trained for a larger class recognition problem towards feature extraction of samples of several smaller class recognition problems. In each case, a distinct Support Vector Machine (SVM) was used as the corresponding classifier. In particular, the CNN of the present study is trained using samples of a standard 50-class Bangla basic character database and features have been extracted for 5 different 10-class numeral recognition problems of English, Devanagari, Bangla, Telugu and Oriya each of which is an official Indian script. Recognition accuracies are comparable with the state-of-the-art.

The most general form of handwriting style is mixed cursive and this is the most difficult type in view of its automatic recognition. Similar handwriting style are prevalent in various scripts such as English, Arabic, Bengali etc. Handwriting recognition for such a script gets further difficult whenever its alphabet consists of a large number of characters like Bengali which has around 350 characters. Hidden Markov models (HMM) are the most popularly used architectures for similar recognition problems. However, the task becomes easy if the underlying lexicon depending upon the specific application is provided. In such situations, holistic or word-based recognition approach is adopted which does not require recognition of the constituent characters. On the other hand, the same task gets complicated as the lexicon size increases and / or it consists of many similar shape words. In a recent study [1] of similar situation, a fully connected non-homogeneous HMM has been used where its observation sequence was generated through explicit segmentation of the input word. In the present study, we have explored that the performance of this HMM-based recognition scheme is independent of both the script and the particular intelligent segmentation strategy. We implemented a novel segmentation scheme based on Discrete Curve Evolution algorithm [2] and two other existing segmentation methods on standard databases of English, Arabic and Bangla to arrive at the above conclusion. Statistical hypothesis testings of the simulation results further confirm the above claim.

OCR errors hurt retrieval performance to a great extent. Research has been done on modelling and correction of OCR errors. However, most of the existing systems use language dependent resources or training texts for studying the nature of errors. Not much research has been reported on improving retrieval performance from erroneous text when no training data is available. We propose a novel algorithm for detecting OCR errors and improving retrieval performance on an E-Discovery corpus. Our contribution is two-fold : (1) identifying erroneous variants of query terms for improvement in retrieval performance, and (2) presenting a scope for a possible error-modelling in the erroneous corpus where clean ground truth text is not available for comparison. Our algorithm does not use any training data or any language specific resources like thesaurus. It also does not use any knowledge about the language except that the word delimiter is blank space. The proposed approach obtained statistically significant improvements in recall over state-of-the-art baselines.

In this paper, we present our recent study of a data driven approach to combining multiple SVM classifiers with RBF kernels each being trained with a distinct feature vector. The SVM classifiers in our ensemble are ranked based on their increasing order of average performance on the validation sample sets. The outputs of the SVM classifiers are combined based on a weighted average strategy which uses the above ranks of the underlying SVMs to determine the respective weights. In the present study, we design four sets of different feature vectors representing online handwritten words. Simple concatenation of these feature vectors does not help much in improving the recognition accuracy compared to the best performing feature vector among the four. Thus, we train distinct SVM classifiers with different feature vectors and combine their outputs at the final stage. The proposed recognition strategy is implemented on a limited vocabulary recognition problem of unconstrained mixed cursive online handwritten Bangla words. It improves existing recognition accuracies on a moderately large database of similar word samples.

Core region detection of handwritten cursive words is an important step towards their automatic recognition. Several preprocessing operations such as height normalization, slant estimation etc. Are often based on this core region. This is particularly useful for word recognition of major Indian scripts, which have large character sets. The main parts of majority of these characters belong to the core region that is bounded above by a headline and bounded below by an imaginary base line. Only a few such characters or their parts appear either above or below the core region. A few approaches are available in the literature for detection of such a core region of offline handwritten word samples of Latin script. Also, a similar region is often determined for recognition of images of printed Indian scripts. However, none of these approaches have studied detection of core region of an unconstrained online handwritten word. In this article, we propose a novel method for detection of the core region of online handwritten word samples of Bangla, a major Indian script. For this we first perform smoothing on the samples and then segment a stroke into sub strokes. We compute certain novel positional features from each such sub stroke. Using these features, a multilayer perceptron (MLP) is trained by back propagation (BP) algorithm. On the basis of the output of the MLP, we determine the position of both the headline and the baseline. We have tested this approach on a recently developed large database of online unconstrained handwriting Bangla word samples. The proposed approach would also work on similar samples of Devanagari, another major Indian script. Experimental results are encouraging.

In this article, a novel lightweight user-adaptive online handwriting recognition scheme has been presented. The present recognition approach is stroke order free. It is based on prior identification of the set of various strokes of different shapes used in writing the characters of the underlying alphabet utilizing a representative sample database. In this approach a very small number of prototypes of each stroke shape is used along with certain weighted DTW distance based nearest neighbour classifier to recognize the strokes in the input character. Individual characters are identified using a look-up table (LUT) each row of which corresponds to one character composed of a distinct set of stroke shapes. This LUT is formed using the representative sample set of the underlying character set. If a stroke does not find a close match in the training set or if the set of strokes for an input character does not find a corresponding entry in the LUT, user adaptation takes place using a modified Learning Vector Quantization (LVQ) method. The proposed scheme has been implemented in an Android-based Bangla handwriting recognizer [1] for handheld devices and its recognition performance is encouraging.

In this article, we describe our recent study of a novel combination of two feature vectors for holistic recognition of offline handwritten word images. In the literature, both contour and skeleton based feature representations have been studied for offline handwriting recognition purpose. However, to the best of our knowledge, there is no such study in which combination of the two feature representations have been considered for the purpose. In the proposed recognition scheme, we use multiclass SVM as the classifier. We have implemented the proposed approach for holistic recognition of Devanagari handwritten town names and tested its performance on a large handwritten word sample database of 100 Indian town names written in Devanagari. Experimental results show sharp improvement in recognition accuracy over the use of any of the individual feature representation schemes. The proposed approach is script independent and can be used for development of a holistic handwritten word image recognition of any script.

Automatic query expansion (AQE) is a useful technique for enhancing the effectiveness of information retrieval systems. In this article, we propose a novel AQE algorithm which first adopts a systematic incremental approach to choose feedback documents from the top retrieved set and then selects the expansion terms aggregating the scores from each feedback set. We also devise a term selection measure and a number of weighting schemes based on easily computable features. A set of experiments with a large number of standard test collections reveals that the proposed incremental blind feedback algorithm outperforms a number of state-of-the-art query expansion methods with remarkable significance and consistency.

In this article, we propose a novel scheme for online handwritten character recognition based on Levenshtein distance metric. Both shape and position information are considered in our feature representation scheme. The shape information is encoded by a string of quantized values of angular displacements between successive sample points along the trajectory of the handwritten character. The consecutive occurrences of same value in such a string are removed retaining only one of them. Next, each element in the resulting string is assigned an integral weight value proportional to the length of the segment of the trajectory represented by the corresponding element. Similarly, position information is encoded by another string of quantized positional information along with their respective weight values. We formulated a distance function based on Levenshtein metric to compute the similarity between an unknown character sample and each training sample. Here, we have also studied the effect of pruning the training sample set based on the above distance between individual training samples of the same character class. The proposed approach has been simulated on different publicly available sample databases of online handwritten characters. The recognition accuracies are acceptable.

This paper presents a statistical model based scheme for automatic extraction of text components from digital images. The work is composed of two tasks. First, we perform segmentation of a color image by applying a pair-copula based mixture model. This produces a number of spatially connected components (some of which may be text). From each of these components, we extract certain features that could discriminate text from non-text components. The feature vectors, arising from text components, are assumed to be random samples from a pair-copula based multivariate distribution. This distribution parameters can be estimated using training text samples (i.e., connected components). Here, we use the ICDAR 2011 "Born-Digital Images'' data set since it provides such ground truth text components. We estimate distribution parameters based on the feature vectors obtained from these training text components. The final task remain is to infer whether a test sample is a text component. We apply a non-parametric statistical hypothesis testing to assess whether a test sample is generated from the known multivariate distribution. If so, we may regard the sample to be a text. Our results obtained on ICDAR 2011 "Born-Digital Images'' data set, are satisfactory.

Stemming is a widely used technique in information retrieval systems to address the vocabulary mismatch problem arising out of morphological phenomena. The major shortcoming of the commonly used stemmers is that they accept the morphological variants of the query words without considering their thematic coherence with the given query, which leads to poor performance. Moreover, for many queries, such approaches also produce retrieval performance that is poorer than no stemming, thereby degrading the robustness. The main goal of this article is to present corpus-based fully automatic stemming algorithms which address these issues. A set of experiments on six TREC collections and three other non-English collections containing news and web documents shows that the proposed query-based stemming algorithms consistently and significantly outperform four state of the art strong stemmers of completely varying principles. Our experiments also confirm that the robustness of the proposed query-based stemming algorithms are remarkably better than the existing strong baselines.

In this paper we introduce a stroke based lexicon reduction technique in order to reduce the search space for recognition of handwritten words. The principle of this technique involves mainly two aspects of a word image to constitute a feature vector: one is word-length and the other is shape of the word. The length of the word image is represented by the number of specific vertical strokes present in the word image and, on the other hand, the shape of a word image is realized with the combination of both horizontal and vertical strokes. The experiment has been carried out with a database of 35,700 off-line handwritten Bangla word images. Though our proposed lexicon reduction technique is developed for recognition of Bangla handwritten words, its generalization property can easily be exploited for recognition of handwriting in other scripts also.

Requirement of annotated handwriting samples for the development of relevant recognition algorithms is an established fact. Although such annotated databases of unconstrained handwriting exist for several scripts of a few languages, the same is not true for any of the scripts of India. As far as Indian scripts are concerned a few databases of handwritten isolated characters are publicly available. These include samples of both online and offline handwriting. However, no such publicly available database of unconstrained handwriting in any of the Indian scripts exists. On the other hand, unconstrained handwriting in Bangla, the second most popular among Indian scripts, is mixed cursive in nature unlike the other scripts of India. Thus, annotation of Bangla unconstrained handwriting samples needs special consideration. During the last few years our group at the Indian Statistical Institute, Kolkata has been working towards the development of a large annotated database of online Bangla handwriting samples and has developed a GUI-based semi-automatic scheme for their annotation at character boundary levels and a scheme for XML representation of such annotated data. The present system implemented for annotation of unconstrained handwriting of Bangla may easily be customized for other scripts. Currently this system is in use for annotation of a large database of Bangla unconstrained online handwriting.

A reasonably large database of online handwritten Bangla characters has been developed. Such a handwritten character sample is composed of one or more strokes. Seventy five such stroke classes have been identified on the basis of the varying handwriting styles present in the character database. Each character sample is a sequence of strokes emanating from these stroke classes. Another database of handwritten Bangla strokes has been developed from the character database. This is the first such database for Bangla script. Certain stroke level features are defined on the basis of certain extremum points which represent the stroke shape reasonably well. The proposed character classification method is a two-stage approach. First, a probability distribution is estimated for each stroke class using the stroke features and then an HMM based character classifier is designed using each stroke class as a state. The parameters of both the stroke class distributions and the character class HMMs are estimated on the basis of the training set having 29,951 character samples. The character level recognition accuracy obtained by the proposed method on the test set having 8,616 samples, is 91.85%.

The wide usage of touch-screen based mobile devices has led to a large volume of the users preferring touch-based interaction with the machine, as opposed to traditional input via keyboards/mice. To exploit this, we focus on the Android platform to design a personalized handwriting recognition system that is acceptably fast, light-weight, possessing a user-friendly interface with minimally-intrusive correction and auto-personalization mechanisms. Since cursive writing on smaller screens is not usual, here we study non-cursive handwriting only. The recognition is done at character level using nearest-neighbor matching to a small, automatically user-adaptive and dynamically updating library of character-class template gestures.

We present a stemming algorithm for text retrieval. The algorithm uses the statistics collected on the basis of certain corpus analysis based on the co-occurrence between two word variants. We use a very simple co-occurrence measure that reflects how often a pair of word variants occurs in a document as well as in the whole corpus. A graph is formed where the word variants are the nodes and two word variants form an edge if they co-occur. On the basis of the co-occurrence measure, a certain edge strength is defined for each of the edges. Finally, on the basis of the edge strengths, we propose a partition algorithm that groups the word variants based on their strongest neighbors, that is, the neighbors with largest strengths.

Stemming is a mechanism of word form normalization that transforms the variant word forms to their common root. In an Information Retrieval system, it is used to increase the system’s performance, specifically the recall and desirably the precision. Although its usefulness is shown to be mixed in languages such as English, because morphologically complex languages stemming produces a significant performance improvement. A number of linguistic rule-based stemmers are available for most European languages which employ a set of rules to get back the root word from its variants. But for Indian languages which are highly inflectional in nature, devising a linguistic rule-based stemmer needs some additional resources which are not available. We present an approach which is purely corpus based and finds the equivalence classes of variant words in an unsupervised manner. A set of experiments on four languages using FIRE, CLEF, and TREC test collections shows that our approach provides comparable results with linguistic rule-based stemmers for some languages and gives significant performance improvement for resource constrained languages such as Bengali and Marathi.

A novel graph-based language-independent stemming algorithm suitable for information retrieval is proposed in this article. The main features of the algorithm are retrieval effectiveness, generality, and computational efficiency. We test our approach on seven languages (using collections from the TREC, CLEF, and FIRE evaluation platforms) of varying morphological complexity. Significant performance improvement over plain word-based retrieval, three other language-independent morphological normalizers, as well as rule-based stemmers is demonstrated.

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 world's 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.

Online handwriting recognition of Indian scripts has been drawing increasing attention in recent years. Related research has gained further momentum due to recent planned funding by the Govt. of India towards technology development of Indian languages and scripts. Standard databases of handwritten characters of a few Indian scripts have already become available. These include online handwritten character databases of Bangla, Devanagari, Tamil and Telugu and these are available free of cost on request. In the present paper, we present benchmark recognition results of the above databases of four most popular scripts of the Indian subcontinent based on two existing feature extraction methods viz. point-float and direction code histogram features and three classifiers viz. Nearest Neighbour (NN), Multilayer Perceptron (MLP) and Hidden Markov Model (HMM) to test the effectiveness of the existing classification methods and provide benchmark results for future online handwriting recognition research of these Indic scripts.

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.

This paper presents a recognition system for isolated handwritten Bangla words, with a fixed lexicon, using a left-right hidden Markov model (HMM). A stochastic search method, namely, genetic algorithm (GA) is used to train the HMM. A new shape based direction encoding features has been developed and introduced in our recognition system. Both non-discriminative and discriminative training procedures have been applied iteratively to optimize the parameters of 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).

A novel segmentation based approach is proposed for recognition of offline handwritten Devanagari words. Stroke based features are used as feature vectors. A hidden Markov model is used for recognition at pseudocharacter level. The word level recognition is done on the basis of a string edit distance.

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.

India is a multilingual multiscript country with more than 18 languages and 10 different major scripts. Not enough research work towards recognition of handwritten characters of these Indian scripts has been done. Tamil, an official as well as popular script of the southern part of India, Singapore, Malaysia, and Sri Lanka has a large character set which includes many compound characters. Only a few works towards handwriting recognition of this large character set has been reported in the literature. Recently, HP Labs India developed a database of handwritten Tamil characters. In the present paper, we describe an off-line recognition approach based on this database. The proposed method consists of two stages. In the first stage, we apply an unsupervised clustering method to create a smaller number of groups of handwritten Tamil character classes. In the second stage, we consider a supervised classification technique in each of these smaller groups for final recognition. The features considered in the two stages are different. The proposed two-stage recognition scheme provided acceptable classification accuracies on both the training and test sets of the present database.

This paper presents a pioneering study on automatic dating of handwritten manuscripts. Analysis of handwriting style forms the core of the dating method. Initially, it is hypothesized that a manuscript can be dated, to a certain level of accuracy, by looking at the way it is written. The hypothesis is then verified with real samples of known dates. A general framework is proposed for machine dating of handwritten manuscripts. Experiments on a database containing manuscripts of Gustave Flaubert (1821- 1880), the famous French novelist reports about 62% accuracy when manuscripts are dated within a range of five calendar years with respect to their exact year of writing.

Stemmers attempt to reduce a word to its stem or root form and are used widely in information retrieval tasks to increase the recall rate. Most popular stemmers encode a large number of language-specific rules built over a length of time. Such stemmers with comprehensive rules are available only for a few languages. In the absence of extensive linguistic resources for certain languages, statistical language processing tools have been successfully used to improve the performance of IR systems. In this article, we describe a clustering-based approach to discover equivalence classes of root words and their morphological variants. A set of string distance measures are defined, and the lexicon for a given text collection is clustered using the distance measures to identify these equivalence classes. The proposed approach is compared with Porter's and Lovin's stemmers on the AP and WSJ subcollections of the Tipster dataset using 200 queries. Its performance is comparable to that of Porter's and Lovin's stemmers, both in terms of average precision and the total number of relevant documents retrieved. The proposed stemming algorithm also provides consistent improvements in retrieval performance for French and Bengali, which are currently resource-poor.

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.

A self-organizing neural network model that computes the smallest circle (also called minimum spanning circle) enclosing a finite set of given points was proposed by Datta.3 In the article,3 the algorithm is stated and it is demonstrated by simulation that the center of the smallest circle can be achieved with a given level of accuracy. No rigorous proof was given in support of the simulation results. In this paper, we make a rigorous analysis of the model and mathematically prove that the model converges to the desired center of the minimum spanning circle. A suitable neural network architecture is also designed for parallel implementation of the proposed model. Time complexity of the algorithm is worked out under the proposed architecture. Extension of the proposed model to higher dimensions is discussed and demonstrated with some applications.

The study of the filamentous objects in waste water has gained momentum due to its significant effect in environmental pollution. The paper describes a neural network based skeleton extraction technique for volumetric images of these biofilm objects. These objects require huge computer storage space. One way to economize the storage space is to represent such images in the form of a vector skeleton (a piecewise linear approximation). Such a skeleton preserves the essential structure of the object. The proposed neural network does not start with a predefined net topology. The topology evolves during the learning process on the basis of the input. The present technique has certain advantages over the conventional 3-D thinning techniques. It achieves data reduction at a higher rate. Also, the proposed technique is highly robust to noise and arbitrary rotations of an image.

In this paper we describe a texture segmentation approach without feature computation based on a multilayer perceptron network (MLP). Thus, the users need not bother about the selection and then computation of feature set and hence real-time segmentation may be possible. The basic motivation of the work is the fact that human vision does not consciously compute features for distinguishing different textures in a scene. A single hidden layer MLP network has been found to be most suitable with heuristically chosen input and hidden layer sizes. A method has been used to speedup the learning of the MLP network. The result of segmentation by a trained network usually results in misclassification in the form of speckles. For the removal of such noise an edge-preserving-noise-smoothing technique is proposed. The final segmentation accuracy is well comparable with that of other existing techniques.

Extraction of skeletal shape from a 2D dot pattern is discussed. We use a self-organizing neural network model to get a piecewise linear approximation of a skeleton of the pattern. It is found that even without a proper definition of a skeleton, the proposed algorithm is able to produce skeletons that are quite close to what we intuitively feel it should be. In Kohonen's self-organizing model, the set of processors and their neighbourhoods are fixed. We suggest here some modifications of it in which the set of processors and their neighbourhoods change adaptively.

Road networks are important features of satellite imagery. The main contribution of the present road detection method consists of an effective enhancement technique and an efficient segmentation technique that removes non-road pixels step by step from the image where parameters involved: in each step images are determined by the sensor characteristics (like spatial resolution and spectral range) of the satellite. Also, the segmentation process depends not only on the road contrast but also on the road length. Thus, a low contrast but long road segment does not get removed. We have tested the algorithm on a number of images from IRS and SPOT satellites and the results are satisfactory.

The paper presents a method for detecting linear features in a satellite image. For enhancing these features, a local operation based algorithm is proposed which uses a certain multiplicative score rather than the commonly used additive score. The main contribution is in segmenting the linear features in the enhanced image where an adaptive segmentation technique is proposed on the basis of statistical models. A robust parameter estimation is done for this purpose.

Two algorithms for propagation and rotation of 2-D objects are proposed. These geometric operations have certain important shape-preserving properties. The factor of propagation and the angle of rotation can be arbitrary. An extension of these algorithms from 2-D to 3-D is possible, using voxels rather than pixels; the shape-preserving properties of propagation and rotation in 3-D are similar to the Z-D properties.>