Ramanathan L

Bio: Ramanathan L is an academic researcher from VIT University. The author has contributed to research in topics: Handwriting recognition & Tamil. The author has co-authored 1 publications.

Isolated Handwritten Tamil Character Recognition using Convolutional Neural Networks

Abstract: In this digital era, the demand for character recognition systems in various languages has been increasing steadily. Tamil is one of the oldest languages spoken till date and the need for efficient Tamil character recognition systems are now more than ever. This research work proposed a novel architecture of convolutional neural networks for the recognition of handwritten Tamil characters. The approach of using convolutional neural networks for the recognition of handwritten characters differs from other standard approaches in feature extraction. Proposed model was trained with a large dataset containing thousands of handwritten Tamil characters and has produced best results in both training and testing. Proposed trained model has also shown promising recognition rates when tested with different handwritten Tamil characters in both real-time and offline modes. The proposed work achieved a training accuracy of approximately 97%, which was significantly higher when compared to other approaches.

Ancient Tamil Character Recognition from Stone Inscriptions – A Theoretical Analysis

Abstract: Character recognition on inscriptions is an area which explores our knowledge of an ancient language. Inscriptions were done in all kinds of environments. This work focuses on recognizing Tamil language characters on stone-based images. From the inscription images, we come to know about the importance of old century languages. Some of the general challenges researchers face in recognizing the characters in stone inscriptions are differentiating the foreground pixel from the background stone images, perspective distortion, different light illumination, the same kind of background/foreground, damaged stones, lack of shape and size of the text. Despite the different ways proposed by the researchers, obstacles and issues continue to exist. This survey attempts to give a detailed analysis of the recent research works on character recognition from the images of stone inscriptions with a special reference to Tamil. It details the methods applied for preprocessing, feature extraction and classification. It gives a road map for future researchers who wish to carry out research in this area.

Ancient Tamil Character Recognition from Stone Inscriptions – A Theoretical Analysis

Abstract: Character recognition on inscriptions is an area which explores our knowledge of an ancient language. Inscriptions were done in all kinds of environments. This work focuses on recognizing Tamil language characters on stone-based images. From the inscription images, we come to know about the importance of old century languages. Some of the general challenges researchers face in recognizing the characters in stone inscriptions are differentiating the foreground pixel from the background stone images, perspective distortion, different light illumination, the same kind of background/foreground, damaged stones, lack of shape and size of the text. Despite the different ways proposed by the researchers, obstacles and issues continue to exist. This survey attempts to give a detailed analysis of the recent research works on character recognition from the images of stone inscriptions with a special reference to Tamil. It details the methods applied for preprocessing, feature extraction and classification. It gives a road map for future researchers who wish to carry out research in this area.

Temple Inscriptions Recognition and Transliteration in Devanagari Script

Abstract: Ancient inscriptions, palm scripts, manuscripts, etc., have vital information about India's rich culture. Recognition and understanding of these inscriptions have been challenging for epigraphers and professionals. The goal of the proposed research is to advance optical character recognition methods for archival Vatteluttu script inscriptions, which date back to the 4th or 5th century AD. This paper discusses a deep learning model to transliterate the ancient Tamil inscriptions (Vatteluttu Script), which can be extended further to other languages. The proposed work is beneficial to epigraphists, archaeological researchers, and the general public who are interested in this topic. The developed deep learning model has achieved an accuracy of 84.12%.

End-To-End Deep-Learning-Based Tamil Handwritten Document Recognition and Classification Model

Abstract: Overview: Handwriting recognition (HR) involves converting handwritten text into machine-readable text. Tamil handwritten document recognition remains a challenging process in various text real-world applications owing to the differences in the sizes, styles and orientation angles of Tamil alphabets. Prior studies concentrated only on character-level segmentation, and each character was subsequently classified. The recently developed machine learning (ML) and deep learning (DL) approaches can be utilized for Tamil handwritten character recognition (HCR). Objective: This paper attempts to present an end-to-end DL-based Tamil handwritten document recognition (ETEDL-THDR) model. Methods: Segmentation is used, first at the word level and then at the line level. ETEDL-THDR text recognition can be accomplished using two modules: line segmentation and line recognition. Initially, the text ETEDL-THDR model targets improving input image quality using the median filtering (MF) technique. To create meaningful regions, more line and character segmentation activities are performed. A deep convolutional neural network (DCNN) based MobileNet approach is also applied to derive feature vectors. Finally, the water strider optimization (WSO) algorithm with a bidirectional gated recurrent unit (BiGRU) model is used to identify the Tamil characters. Results: Extensive experimental analyses of the text ETEDL-THDR model have been carried out, and the results show that the text ETEDL-THDR model performs better than more recent methodologies, with a maximum accuracy of 98.48%, a precision of 98.38%, a sensitivity of 97.98%, specificity of 98.27% and text F-measure of 98.35%. Conclusion: The comparison results show that the proposed model can recognize Tamil handwritten documents in real time.