Popular Scripts Research Articles

Word-Level Script Identification Using Texture Based Features

Script identification is an appealing research interest in the field of document image analysis during the last few decades. The accurate recognition of the script is paramount to many post-processing steps such as automated document sorting, machine translation and searching of text written in a particular script in multilingual environment. For automatic processing of such documents through Optical Character Recognition (OCR) software, it is necessary to identify different script words of the documents before feeding them to the OCR of individual scripts. In this paper, a robust word-level handwritten script identification technique has been proposed using texture based features to identify the words written in any of the seven popular scripts namely, Bangla, Devanagari, Gurumukhi, Malayalam, Oriya, Telugu, and Roman. The texture based features comprise of a combination of Histograms of Oriented Gradients (HOG) and Moment invariants. The technique has been tested on 7000 handwritten text words in which each script contributes 1000 words. Based on the identification accuracies and statistical significance testing of seven well-known classifiers, Multi-Layer Perceptron (MLP) has been chosen as the final classifier which is then tested comprehensively using different folds and with different epoch sizes. The overall accuracy of the system is found to be 94.7% using 5-fold cross validation scheme, which is quite impressive considering the complexities and shape variations of the said scripts. This is an extended version of the paper described in (Singh et al., 2014).

Recognition of archaic Lanna handwritten manuscripts using a hybrid bio-inspired algorithm

This paper proposes a new character recognition system for archaic Lanna handwritten characters. The proposed system consists of two main processes: the segmentation process and the recognition process. The segmentation process decomposes the touching or overlapping characters, commonly found in Lanna manuscripts, into isolated characters. In the recognition process, the proposed system uses the self-organizing map to divide the input images into several clusters. The clonal selection algorithm of the artificial immune system is then used to build a recognition model for each cluster created by the self-organizing map. Finally, the particle swarm optimization is employed as a local search mechanism. The proposed system was evaluated and compared to several state-of-the-art approaches. The experimental results demonstrate that the proposed system is very effective in recognizing not only Lanna characters but also the handwritten numerals of the five most popular scripts.

A survey on optical character recognition for Bangla and Devanagari scripts

The past few decades have witnessed an intensive research on optical character recognition (OCR) for Roman, Chinese, and Japanese scripts. A lot of work has been also reported on OCR efforts for various Indian scripts, like Devanagari, Bangla, Oriya, Tamil, Telugu, Malayalam, Kannada, Gurmukhi, Gujarati, etc. In this paper, we present a review of OCR work on Indian scripts, mainly on Bangla and Devanagari—the two most popular scripts in India. We have summarized most of the published papers on this topic and have also analysed the various methodologies and their reported results. Future directions of research in OCR for Indian scripts have been also given.

A statistical–topological feature combination for recognition of handwritten numerals

Principal Component Analysis (PCA) and Modular PCA (MPCA) are well known statistical methods for recognition of facial images. But only PCA/MPCA is found to be insufficient to achieve high classification accuracy required for handwritten character recognition application. This is due to the shortcomings of those methods to represent certain local morphometric information present in the character patterns. On the other hand Quad-tree based hierarchically derived Longest-Run (QTLR) features, a type of popularly used topological features for character recognition, miss some global statistical information of the characters. In this paper, we have introduced a new combination of PCA/MPCA and QTLR features for OCR of handwritten numerals. The performance of the designed feature-combination is evaluated on handwritten numerals of five popular scripts of Indian sub-continent, viz., Arabic, Bangla, Devanagari, Latin and Telugu with Support Vector Machine (SVM) based classifier. From the results it has been observed that MPCA+QTLR feature combination outperforms PCA+QTLR feature combination and most other conventional features available in the literature.

Handwritten Script Recognition using DCT, Gabor Filter and Wavelet Features at Line Level

In a country like India where more number of scripts are in use, automatic identification of printed and handwritten script facilitates many important applications including sorting of document images and searching online archives of document images. In this paper, a multiple feature based approach is presented to identify the script type of the collection of handwritten documents. Eight popular Indian scripts are considered here. Features are extracted using Gabor filters, Discrete Cosine Transform, and Wavelets of Daubechies family. Experiments are performed to test the recognition accuracy of the proposed system at line level for bilingual scripts and later extended to trilingual scripts. We have obtained 100% recognition accuracy for bi-scripts at line level. The classification is done using k-nearest neighbour classifier.

Visual gene developer: a fully programmable bioinformatics software for synthetic gene optimization

BackgroundDirect gene synthesis is becoming more popular owing to decreases in gene synthesis pricing. Compared with using natural genes, gene synthesis provides a good opportunity to optimize gene sequence for specific applications. In order to facilitate gene optimization, we have developed a stand-alone software called Visual Gene Developer.ResultsThe software not only provides general functions for gene analysis and optimization along with an interactive user-friendly interface, but also includes unique features such as programming capability, dedicated mRNA secondary structure prediction, artificial neural network modeling, network & multi-threaded computing, and user-accessible programming modules. The software allows a user to analyze and optimize a sequence using main menu functions or specialized module windows. Alternatively, gene optimization can be initiated by designing a gene construct and configuring an optimization strategy. A user can choose several predefined or user-defined algorithms to design a complicated strategy. The software provides expandable functionality as platform software supporting module development using popular script languages such as VBScript and JScript in the software programming environment.ConclusionVisual Gene Developer is useful for both researchers who want to quickly analyze and optimize genes, and those who are interested in developing and testing new algorithms in bioinformatics. The software is available for free download at http://www.visualgenedeveloper.net.

A novel framework for automatic sorting of postal documents with multi-script address blocks

Recognition of numeric postal codes in a multi-script environment is a classical problem in any postal automation system. In such postal documents, determination of the script of the handwritten postal codes is crucial for subsequent invocation of the digit recognizers for respective scripts. The current framework attempts to infer about the script of the numeric postal code without having any bias from the script of the textual address part of the rest of the address block, as they might differ in a potential multi-script environment. Scope of the current work is to recognize the postal codes written in any of the four popular scripts, viz., Latin, Devanagari, Bangla and Urdu. For this purpose, we first implement a Hough transformation based technique to localize the postal-code blocks from structured postal documents with defined address block region. Isolated handwritten digit patterns are then extracted from the localized postal-code region. In the next stage of the developed framework, similar shaped digit patterns of the said four scripts are grouped in 25 clusters. A script independent unified pattern classifier is then designed to classify the numeric postal codes into one of these 25 clusters. Based on these classification decisions a rule-based script inference engine is designed to infer about the script of the numeric postal code. One of the four script specific classifiers is subsequently invoked to recognize the digit patterns of the corresponding script. A novel quad-tree based image partitioning technique is also developed in this work for effective feature extraction from the numeric digit patterns. The average recognition accuracy over ten-fold cross validation of results for the support vector machine (SVM) based 25-class unified pattern classifier is obtained as 92.03%. With randomly selected six-digit numeric strings of four different scripts; an average of 96.72% script inference accuracy is achieved. The average of tenfold cross-validation recognition accuracies of the individual SVM classifiers for the Latin, Devanagari, Bangla and Urdu numerals are observed as 95.55%, 95.63%, 97.15% and 96.20%, respectively.

Bridging Cultures in the Schools

Bridging Cultures in the Schools

Recognition of Printed Arabic Character Using Gray- Scale Matrices

An Optical Character Recognition (OCR) approach for printed Arabic script is presented in this paper, Which is one of the most popular scripts in the world. Development of an OCR system. For Arabic script it is difficult because Arabic characters are distinct and many structurally similar characters exist in the character set. In the proposed approach, the technique can be divided into three major steps. The first step is digitization then do some pre-processing like segmentation to detect the slant of character and correct it .Second, feature extraction , using gray-level matrices. Finally, the K-Nearest-Neighbors is used for classification. This method was tested using 45 patterns for each Arabic character with different fonts (simplified Arabic, tahoma, traditional Arabic), The sample images were divided into 20 training and 25 test images. Images in the test set did not appear in the training sets. This method performs extremely well with recognition rates .90.3%. This is a very good performance. All of this demonstrates that the new method is able to handle printed Arabic character task efficiently. It is a promising technique for recognition printed Arabic character. 1. Introduction Optical character recognition (OCR), deals with the recognition of optically processed character rather magnetically processed ones. In a typical OCR system, input characters are read and digitized by an optical scanner. Each character is then located, segmented and the resulting matrix is fed into a preprocessor. Off-line recognition can de considered the most general case: no special device is required for writing and signal interpretation is independent of signal generation, as in human recognition [6]. The recognition of Arabic character has been an area of great interest for many years, and a number of research papers and reports have already been published in this area. There are several major problems with Arabic character recognition: Arabic characters are distinct and ideographic, many structurally similar character exist in the character set Table (1). Thus, classification criteria are difficult to generate [1 J[3j[6]. The Arabic language has a rich vocabulary. More than 200 million people speak this language as their native speaking, and over 1 billion people use its character set, such as Persian and Urdu, Due to the cursive nature of the script, there are several characteristic that make recognition of Arabic distinct from the recognition of Latin script or Chinese The study of Arabic character recognition has been regarded since 1980s. However, in comparison with the other languages, such as Latin, Chinese and Japanese, there is a little work has been conducted on the automatic recognition of Arabic character [4][5].

»Thinking Ahead«. Fiction as Prediction in Popular Scripts on Political Scenarios

Zusammenfassung Der Beitrag analysiert den wachsenden Einfluß von Produkten der Populärkultur auf reale politische Entscheidungsfindungsprozesse. Dieser Einfluß, den David Mamet in seinem Film über die mediale Simulation eines Kriegs (Wag the Dog, 1998) schillernd ausgestaltet hat, beruht auf der spezifischen Fähigkeit der Unterhaltungsindustrie, spannende Plots und innovative Spezialeffekte zu kreieren. Die beiden Beispiele, die der Beitrag diskutiert, demonstrieren, wie diese Fähigkeit zunehmend als Element politischer Planung betrachtet werden kann. Die Frage, die dabei zur Debatte steht ist, ob dieses Verhältnis lediglich als Import bestimmter Techniken anzusehen ist oder ob die Populärkultur tatsächlich in der Lage ist, zukünftige Szenarien sowie die möglichen Reaktionen auf sie vorwegzunehmen. Im ersten Beispiel arbeitet die US Army am Institute for Creative Technology (USC) mit Drehbuchautoren aus Hollywood sowie Computerspielproduzenten zusammen, um ihre Soldaten durch virtuelle Trainingsprogramme auf ungewohnte Einsätze vorbereiten zu können. Das zweite Beispiel, Tom Clancys Thriller Debt of Honor (1995), entwirft das Szenario eines internationalen Konflikts nach dem Kalten Krieg und scheint dabei in der Schlußszene des Romans die Ereignisse des 11. September vorwegzunehmen. Der Beitrag schlägt vor, den Grund für diese vermeintlich >prophetische< Fähigkeit des Buchs in der hohen Geschwindigkeit zu sehen, mit welcher die Populärkultur Szenarien für nahezu alle möglichen Konflikte der Zukunft durchspielen kann. Gleichzeitig benutzt die Populärkultur Klischees und vertraute Topoi, mit deren Hilfe die politischen Katastrophen der Zukunft vorstellbar gemacht werden.

Segmentation of touching characters in printed devnagari and bangla scripts using fuzzy multifactorial analysis

One of the important reasons for poor recognition rate in optical character recognition (OCR) system is the error in character segmentation. Existence of touching characters in the scanned documents is a major problem to design an effective character segmentation procedure. In this paper, a new technique is presented for identification and segmentation of touching characters. The technique is based on fuzzy multifactorial analysis. A predictive algorithm is developed for effectively selecting possible cut columns for segmenting the touching characters. The proposed method has been applied to printed documents in Devnagari and Bangla: the two most popular scripts of the Indian sub-continent. The results obtained from a test-set of considerable size show that a reasonable improvement in recognition rate can be achieved with a modest increase in computations.

A complete printed Bangla OCR system

A complete Optical Character Recognition (OCR) system for printed Bangla, the fourth most popular script in the world, is presented. This is the first OCR system among all script forms used in the Indian sub-continent. The problem is difficult because (i) there are about 300 basic, modified and compound character shapes in the script, (ii) the characters in a word are topologically connected and (iii) Bangla is an inflectional language. In our system the document image captured by Flat-bed scanner is subject to skew correction, text graphics separation, line segmentation, zone detection, word and character segmentation using some conventional and some newly developed techniques. From zonal information and shape characteristics, the basic, modified and compound characters are separated for the convenience of classification. The basic and modified characters which are about 75 in number and which occupy about 96% of the text corpus, are recognized by a structural-feature-based tree classifier. The compound characters are recognized by a tree classifier followed by template-matching approach. The feature detection is simple and robust where preprocessing like thinning and pruning are avoided. The character unigram statistics is used to make the tree classifier efficient. Several heuristics are also used to speed up the template matching approach. A dictionary-based error-correction scheme has been used where separate dictionaries are compiled for root word and suffixes that contain morpho-syntactic informations as well. For single font clear documents 95.50% word level (which is equivalent to 99.10% character level) recognition accuracy has been obtained. Extension of the work to Devnagari, the third most popular script in the world, is also discussed.

Computer recognition of printed Bangla script

This paper considers optical character recognition (OCR) of Bangla, the second most popular script in the Indian subcontinent. A complete OCR system is described for documents of single Bangla font, where more than three hundred character shapes are recognized by a combination of template and feature-matching approach. Here the document image captured by a flatbed scanner is subject to tilt correction, line, word and character segmentation, simple and compound character separation, feature extraction and finally character recognition. Some character occurrence statistics have been computed to aid the recognition process. The simple character recognition is done by a feature-based tree classifier, and the compound character recognition involves a template matching approach preceded by a feature-based grouping. At present, recognition accuracy of about 96% is obtained by the system.