Methods based on learning from examples that have been widely applied to character recognition are considered as one of the classification methods. This class of methods includes statistical methods, artificial neural network, support vector machines, multiple classifier combinations, etc. among these, artificial neural network are quite popular and have gained more attention. The paper presents a survey on ANNs applied for recognizing characters and the features applied as inputs. The principal objective of this paper is to help researchers attempt to apply ANN for character recognition and analysis of the networks and the features extracted to improve classification accuracy.

Character Recognition using Approaches of Artificial Neural Network: A Review

Feature extraction and classification techniques for handwritten Devanagari text recognition: a survey

Recognition of handwritten characters from Devanagari, Bangla, and Odia languages using transfer-learning-based VGG-16 networks

In the discipline of pattern recognition, optical character recognition is a critical task. A significant amount of research has been done on character recognition in the English language but in the Indian context, the research has been limited. Devanagari is a commonly used Indian script that is the foundation of languages like Hindi, Sanskrit, Kashmiri, and Marathi. Several researchers have published their work on this topic in recent years with some promising results. To expand upon the existing work and to provide a benchmark for future studies, a comparative study of four different classifiers and two different feature extraction techniques have been proposed in this paper. Multi-Layer Perceptron, K-Nearest Neighbor, Support Vector Machine, and Random Forest algorithms are used as classifiers whereas Convolutional Neural Network and Histogram of Oriented Gradients are used as feature extraction techniques.

A comparative Study of Handwritten Devanagari Script Character Recognition Techniques

Optical character recognition techniques are capable of automatic translating of document images into equivalent character codes, so it helps in saving human energy as well as cost. These techniques can play a key role to improve or enhance the interaction between human and machine in many applications such as postal automation, signature verification, recognition of city names and automatic bank cheque processing/reading. This paper gives a review of various techniques explored for Devanagari word/text and isolated character recognition in the past few years. Different challenges to optical character recognition are also presented in this work. In the end, practical aspects towards the development of a robust optical character recognition system has been discussed along with directions for future research.

Sukhjinder Singh

Papers

Review of Optical Devanagari Character Recognition Techniques