International Journal of Innovative Technology and Exploring Engineering (IJITEE) ISSN: 2278-3075, Volume-8 Issue-9, July 2019 1677 Published By: Blue Eyes Intelligence Engineering & Sciences Publication Retrieval Number: I8099078919/19©BEIESP DOI:10.35940/ijitee.I8099.078919 Augmentation of Local, Global Feature Analysis for online Character Recognition System for Telugu Language using Feed Forward Neural Networks (FFNN) Goda Srinivasarao, Rajeswara Rao Ramisetty Abstract: In this paper, we propose ANN based online handwritten character recognition for Telugu Language. In literature review, it is observed that Size of the database and preprocessing approaches plays prominent role in the recognition performance. Preprocessing techniques like normalization, interpolation, Uniformization, Smoothing, Slant Correction and resampling techniques are performed for better recognition performance. Local features like(x,y)co-ordinates, ) , ( y x   ) , ( 2 2 y x   and the global features like tan() are considered as features for ANN modeling and Classification of 52 Telugu vowels and consonants. Recognition performance is evaluated by augmentation the local, global features and and tan () Features. Theperformance is evaluated in terms of precision, recall and F-measure. Significant Improvement is reported by augmentation andby adopting preprocessing techniques. The database used for the study is HP-online Telugu database. Index Terms — ANN, HP-database, local features, global features I. INTRODUCTION Though languages like English can be or given as an input to the computers to execute as commands or process the data. It is not the same for quite a few languages like Telugu, Chinese, Hindi and other Indian or Japanese languages. Because these languages involve lot of stroke variations from writer to writer. But, rather than giving input via keyboard or voice, it is advisable to give it via handwritten samples (like parchments of paper or electronic pens). For instance, entering data into the database from the hand-filled Railway-reservation applications is a tedious task and can be automated. Moreover, properly trained systems will be capable of recognizing the hand-written text better than that of the human. And this handwriting recognition is plays a crucial role in the human computer interaction model. Efforts have already been made to build system in both online and offline fields for achieving various aims, like recognizing numeric characters, language recognitions like Assamese [2], Thai [5], and Arabic [4]. Revised Manuscript Received on July 05, 2019. Goda Srinivasarao, Research Scholor, Department of Computer Science & Engineering, JNTUA-Anantapuramu, A.P, India Rajeswara Rao Ramisetty, Professor, Department of Computer Science & Engineering, JNTUK-Unviersity College of Engineering-Vizianagaram Unlike English, the basic characters in Telugu script consist of 16 vowels and 36 consonants. The characters in telugu script are a combination of these basic characters and their modifiers which gives rise to about 18,000 unique characters. All these unique characters in Telugu can be represented as a combination of a manageable set of 235 strokes. Also the character strokes, other the first stroke taken as main stroke, can be divided, based on the position of the stroke, into three - top stroke, bottom stroke and baseline stroke. As a preliminary attempt, we use character based recognition for on-line handwriting recognition of Telugu which is a very popular south Indian language, in which much research has not yet done. Telugu language found in the South Indian states of Andhra Pradesh and Telangana as well as several other neighboring states. Subset Telugu symbols given in the Figure 2. In Telugu script, many of the characters resemble one another in structure. The framework for online handwritten character recognition is depicted in Figure 2.Further, many users write two or more characters in a similar way which can be difficult to classify correctly. In Telugu some of the confusing pairs are there. An SVM based stroke recognition method used in [1] for Telugu characters. Based on proximity analysis, the recognized strokes are mapped onto characters using information of stroke combinations for the script. Each stroke is represented as preprocessed (x, y) coordinates. The data sample size 37817 was collected from 92 users using the SuperpenTM, a product of UC Logic. The observed recognition accuracy is 83%. Importance of annotation of online handwritten data illustrated in [1]. Modular approach for recognition of strokes proposed in [2]. Based on the relative position of strokes in the character, the strokes are categorized into baseline, bottom, top strokes. The recognition model SVM was used for each category separately. The recognition accuracy is high for each stage, when compared to combined classifier. Elastic matching technique, DTW used in [3]. The features used are local features: x-y features, Tangent Angle (TA) and Shape Context (SC) features, Generalized Shape Context (GSC) feature and the fourth set containing (x, y) coordinates, normalized first and second derivatives and curvature features.