Handwritten Character Recognition of a Vernacular Language: The Odia Script

Optical Character Recognition, i.e., OCR taking into account the principle of applying electronic or mechanical translation of images from printed, manually written or typewritten sources to editable version. As of late, OCR technology has been utilized in most of the industries for better management of various documents. OCR helps to edit the text, allow us to search for a word or phrase, and store it more compactly in the computer memory for future use and moreover, it can be processed by other applications. In India, a couple of organizations have designed OCR for some mainstream Indic dialects, for example, Devanagari, Hindi, Bangla and to some extent Telugu, Tamil, Gurmukhi, Odia, etc. However, it has been observed that the progress for Odia script recognition is quite less when contrasted with different dialects. Any recognition process works on some nearby standard databases. Till now, no such standard database available in the literature for Odia script. Apart from the existing standard databases for other Indic languages, in this thesis, we have designed databases on handwritten Odia Digit, and character for the simulation of the proposed schemes. In this thesis, four schemes have been suggested, one for the recognition of Odia digit and other three for atomic Odia character. Various issues of handwritten character recognition have been examined including feature extraction, the grouping of samples based on some characteristics, and designing classifiers. Also, different features such as statistical as well as structural of a character have been studied. It is not necessary that the character written by a person next time would always be of same shape and stroke. Hence, variability in the personal writing of different individual makes the character recognition quite challenging. Standard classifiers have been utilized for the recognition of Odia character set. An array of Gabor filters has been employed for recognition of Odia digits. In this regard, each image is divided into four blocks of equal size. Gabor filters with various scales and orientations have been applied to these sub-images keeping other filter parameters constant. The average energy is computed for each transformed image to obtain a feature vector for each digit. Further, a Back Propagation Neural Network (BPNN) has been employed to classify the samples taking the feature vector as input. In addition, the proposed scheme has also been tested on standard digit databases like MNIST and USPS. Toward the end of this part, an application has been intended to evaluate simple arithmetic equation. viii A multi-resolution scheme has been suggested to extract features from Odia atomic character and recognize them using the back propagation neural network. It has been observed that few Odia characters have a vertical line present toward the end. It helps in dividing the whole dataset into two subgroups, in particular, Group I and Group II such that all characters in Group I have a vertical line and rest are in Group II. The two class classification problem has been tackled by a single layer perceptron. Besides, the two-dimensional Discrete Orthogonal S-Transform (DOST) coefficients are extracted from images of each group, subsequently, Principal Component Analysis (PCA) has been applied to find significant features. For each group, a separate BPNN classifier is utilized to recognize the character set

Development of Features for Recognition of Handwritten Odia Characters

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

Gesture-based Numeral Extraction and Recognition Shree Prakash

In this work the extraction of numerals and recognition is done using gesture. Gestures are elementary movements of a human body part, and are the atomic components describing the meaningful motion of a person. It is of utmost importance in designing an intelligent and efficient human-computer interface. Two approaches are proposed for the extraction of numeral from gesture. In the first approach, numerals are formed using the finger gesture. The movement of the finger gesture is identified using optical flow method. A view-specific representation of movement is constructed, where movement is defined as motion over time. A temporal encoding is performed from different frames into a single frame. To achieve this we utilize motion history image (MHI) scheme which spans the time scale of gesture. In the second approach, gesture is performed by the use of a pointer like a pen whose tip is either red, green, or blue. In the scene multiple persons are present performing various activities, but our scheme only captures the gesture made by the desired object. HSI color model is used to segment the tip followed by the optical flow to segment the motion. After getting the temporal template, the features are extracted and the recognition is performed. Our second approach is invariant to uninteresting movements in the surrounding while capturing the gesture. Hence it will not affect the final result of recognition

Gesture Based Character Recognition

Gesture is rudimentary movements of a human body part, which depicting the important movement of an individual. It is high significance for designing efficient human-computer interface. An proposed method for Recognition of character(English alphabets) from gesture i.e gesture is performed by the utilization of a pointer having color tip (is red, green, or blue). The color tip is segment from back ground by converting RGB to HSI color model. Motion of color tip is identified by optical flow method. During formation of multiple gesture the unwanted lines are removed by optical flow method. The movement of tip is recoded by Motion History Image(MHI) method. After getting the complete gesture, then each character is extracted from hand written image by using the connected component and the features are extracted of the correspond character. The recognition is performed by minimum distance classifier method (Modified Hausdorf Distance). An audio format of each character is store in data-set so that during the classification, the corresponding audio of character will play

Religious Individualisation

This volume brings together key findings of the research project ‘Religious Individualisation in Historical Perspective’ at the Max Weber Centre for Advanced Cultural and Social Studies. Combining a wide range of disciplinary approaches, methods and theories, the volume assembles over 50 contributions that explore and compare processes of religious individualisation in Asia, the Mediterranean, and Europe from antiquity to the recent past

Degree zero art: Piero Manzoni and Hélio Oiticica

This thesis seeks to unfold the concept of the ‘degree zero art’ as an artistic and cultural project as manifested in the practices of two very different artists, Milan-based Piero Manzoni (Soncino 1933- Milan 1963) and Rio-born Hélio Oiticica (Rio de Janeiro 1937-1980), during the second half of the twentieth century. Despite the clear contrasts between their works and their very different cultural formations, the thesis focuses on these artists in order to show how their practices align around the challenge to aesthetic categories, stylistic labels and political frameworks employed by much recent critical literature. In order to discuss intellectual and critical structures developed to narrate varieties of North American conceptual practices, this thesis proposes a new interpretative frame: a ‘degree zero aesthetics’, creating a transnational dialogue between the work of Manzoni and Oiticica. Borrowing from the understanding of zero proposed by the German Zero group at the beginning of the sixties, I argue that the idea of zero denotes a fresh start and constructive will; it therefore explains the process of erasing and rebuilding from scratch that has characterised the post-war generation. Alongside the process of construing an aesthetic around the notion of ‘zero’, this thesis aims to deconstruct popular sites of discourse around the tropes of ‘participation’ and ‘politics’, critically readdressing the historiography surrounding these themes. Lastly, this project attempts to discuss the literature on both artists, who have become paradigmatic of certain key movements and moments in Latin American and European art respectively, and in recent elaborations of global art histories

Words of Power, the Power of Words. The Twentieth-Century Communist Discourse in International Perspective

This volume proposes a collection of nineteen essays on the history of international communism during the twentieth century. The first part is dedicated to the Italian Communist Party, the most important communist party in Western Europe. The book then moves on to an analysis of the parties of Eastern Europe, for example in the Soviet Union, East Germany, Romania, Czechoslovakia, and Yugoslavia. Finally, the analysis goes beyond the European boundaries, focusing on communism in Latin America, with Chilean communism and the Ecuadorian Left, and in Eastern Asia, with the Vietnamese and the Chinese Communist parties. The book offers a global and interdisciplinary approach, merging the analysis of political-cultural processes with the study of political discourse and language, textual or iconic, thanks to studies by historians, linguists, philosophers, and historians of language.Questo volume, in lingua inglese, propone una raccolta di diciannove saggi sulla storia del comunismo internazionale nel corso del ventesimo secolo. La prima parte è dedicata al Partito comunista italiano, il più importante partito comunista in Europa occidentale. Il libro passa quindi a un'analisi dei partiti dell'Europa orientale, ad esempio in Unione Sovietica, Germania orientale, Romania, Cecoslovacchia e Jugoslavia. Infine, l'analisi va oltre i confini europei, concentrandosi sul comunismo in America Latina, con il comunismo cileno e la sinistra ecuadoriana, e nell'Asia orientale, con i partiti comunista vietnamita e cinese. Il libro offre un approccio globale e interdisciplinare, fondendo l'analisi dei processi politico-culturali con lo studio del discorso politico e del linguaggio, testuale o iconico, grazie a studi di storici, linguisti, filosofi e storici del linguaggio

Strategies to Mitigate Natural Disaster Impact in the Auto Maintenance Business

Many small business owners lack strategies to mitigate natural disasters. Damage caused by natural disasters usually costs millions of dollars in injuries or lost lives, disruption to operations, and property damage. Small business owners who fail to plan and prepare for disaster frequently face closure when disasters strike. The goal of this study was to explore strategies independent auto maintenance business owners use to mitigate natural disasters. Holling’s organizational resilience theory grounded this qualitative multiple case study. Five purposively selected participants who implemented disaster mitigation strategies from Texas, Arkansas, and Louisiana participated in this study. Semistructured interviews were used to collect data, supplemented by triangulation using company documents, strategic plans, financial data, emails, website information, and operation manuals. Yin’s 5-step data analysis process yielded four themes: employee relations and financial strength, disaster planning and response guideline, communication, and collaboration and partnership. The key recommendation for business owners is to understand, plan, and execute successful natural disaster mitigation strategies to ensure business continuity and resilience. The implication for positive social change is the potential for businesses to avoid permanent business closure, create jobs, retain employees, and improve the economic standard of living for communities