Given the ubiquity of handwritten documents in human transactions, Optical Character Recognition (OCR) of documents have invaluable practical worth. Optical character recognition is a science that enables to translate various types of documents or images into analyzable, editable and searchable data. During last decade, researchers have used artificial intelligence/machine learning tools to automatically analyze handwritten and printed documents in order to convert them into electronic format. The objective of this review paper is to summarize research that has been conducted on character recognition of handwritten documents and to provide research directions. In this Systematic Literature Review (SLR) we collected, synthesized and analyzed research articles on the topic of handwritten OCR (and closely related topics) which were published between year 2000 to 2019. We followed widely used electronic databases by following pre-defined review protocol. Articles were searched using keywords, forward reference searching and backward reference searching in order to search all the articles related to the topic. After carefully following study selection process 176 articles were selected for this SLR. This review article serves the purpose of presenting state of the art results and techniques on OCR and also provide research directions by highlighting research gaps.

Handwritten Optical Character Recognition (OCR): A Comprehensive Systematic Literature Review (SLR)

In recent data, Optical character recognition (OCR) systems have laid hands in the field of most popular language recognitions. Unlike other languages, the Tamil language is more complex to recognize, and hence considerable efforts have been laid in literature. However, the models are not yet well-organized for precise recognition of Tamil characters. Thus, the current research work develops a novel Tamil Handwritten Character Recognition approach by following two major processes, viz. pre-processing and recognition. The pre-processing phase encloses RGB to grayscale conversion, binarization with thresholding, image complementation, morphological operations, and linearization. Subsequently, the pre-processed image after linearization is subjected to recognition via an optimally configured Convolutional Neural Network (CNN). More particularly, the fully connected layer and weights are fine-tuned by a new Self Adaptive Lion Algorithm (SALA) that is the conceptual improvement of the standard Lion Algorithm (LA). The performance of the proposed work is compared and proved over other state-of-the-art models with respect to certain performance measures.

https://link.springer.com/content/pdf/10.1007/s11042-020-09771-z.pdf

Optimally configured convolutional neural network for Tamil Handwritten Character Recognition by improved lion optimization model

MAT-AGCA: Multi Augmentation Technique on small dataset for Balinese character recognition using Convolutional Neural Network

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.

Isolated Handwritten Tamil Character Recognition using Convolutional Neural Networks

This paper aims the study and implementation of Convolutional Neural Network (CNN) for Image character recognition. Handwritten Character Recognition involves recognition of texts present in digital images and documents and processing them for various applications such as machine translation, pattern recognition and so on. This paper studies the use of CNN in detecting and recognizing handwritten text images with a higher accuracy. The CNN model is tested on English handwritten characters and validated on its performance. The model performs feature extraction from images through multiple layers. These are later used for training the model and thereby recognizing characters.

Image Character Recognition using Convolutional Neural Networks

Character recognition is developed for various patterns of handwritten or optical characters to be recognized digitally. There are many Tamil literatures in undigitized form. Using deep learning the undigitized Tamil literatures can be converted into readable format. Many researches were carried on character recognition using deep learning for languages like Arabic, Devanagari, Telugu, etc… Due to the larger category set and confusion in similarities between handwritten characters Tamil character recognition is a challenge. In this paper, we propose a character recognition system for handwritten Tamil characters using deep learning. Here, VGG 16 approaches is carried out. The proposed work gives efficiency of 94.52% on our datasets.

A. Shavar Banu

Papers

Handwritten Tamil Character Recognition UsingDeep Learning