Recognition of Hand Printed Characters Based on Simple Geometric Features (original) (raw)

Handwritten Character Recognition: A Comprehensive Review on Geometrical Analysis

This paper presents a detailed review of Offline Handwritten Character Recognition. HCR is an optical character recognition, which convert the human readable character to machine readable format. In HCR, to attain 99% accuracy is very difficult. Here a detailed study on Geometrical methods of feature extraction in character recognition has been done by giving more emphasis to Zone based techniques and it has been analyzed that the efficiency of HCR depends on the selection of appropriate feature extraction methods and classifier. A comparative study in various steps in character recognition like Preprocessing, Segmentation, Feature Extraction and Classification are carried out. Various application areas of HCR like Postal address reading, mail sorting, office automation for text entry, person identification, signature verification, bank-check processing etc. are also analyzed.

A Comprehensive Study On Handwritten Character Recognition System

Nowadays handwritten character recognition is still remain an open problem because of the variability in writing style. Conversion of handwritten characters is important for making manuscripts into machine recognizable form so that it can be easily accessed and preserved. Many researchers have worked in the area of handwriting recognition and numerous techniques and models have been developed to recognize handwritten text. The study investigates that in any character recognition system there exist three major stages such as Preprocessing, Feature Extraction and Classification. This paper provides a comprehensive review of existing works in offline handwritten character recognition.

Effective Handwriting Recognition System Using Geometrical Character Analysis Algorithms

Lecture Notes in Computer Science, 2012

We propose a new method for natural writing recognition that utilizes geometric features of letters. The paper deals with recognition of isolated handwritten characters using an artificial neural network. As a result of the geometrical analysis realized, graphical representations of recognized characters are obtained in the form of pattern descriptions of isolated characters. The radius measurements of the characters obtained are inputs to the neural network for natural writing recognition which is font independent. In this paper, we present a new method for off-line natural writing recognition and also describe our research and tests performed on the neural network.

A Survey on Handwritten Character Recognition

— At current years Handwritten Character Recognition is main significant and admired research sector in the part of Image processing. In Handwritten edition there is no constraint on the writing style. Handwritten letters are not easy to recognize due to diverse human handwriting style, size and shape of letters. In a Handwritten character recognition, the set of geographies plays as foremost issues, as method in choosing the related feature that profits minimum classification fault. Handwriting recognition is most challenging area if image and pattern recognition. Handwriting recognition is very useful in real world. Text recognition in the handwritten documents has been studied as one of the projecting research areas by different researchers during the last few decades.

Geometrical Features Based Approach for the Classification and Recognition of Handwritten Characters

2009

Various approaches have evolved in the past and intensive research is still being carried out, at present. In this paper, we have presented a geometrical feature based approach to recognize handwritten characters. The strength of our approach is in the comprehensive classification scheme due to which, we have been able to achieve a recognition rate of 95.8%, better than the previous approaches.

A Literature Survey on Handwritten Character Recognition

2015

Handwriting recognition has gained a lot of attention in the field of pattern recognition and machine learning due to its application in various fields. Optical Character Recognition (OCR) and Handwritten Character Recognition (HCR) has specific domain to apply. Various techniques have been proposed to for character recognition in handwriting recognition system. Even though, sufficient studies and papers describes the techniques for converting textual content from a paper document into machine readable form. In coming days, character recognition system might serve as a key factor to create a paperless environment by digitizing and processing existing paper documents. This paper presents a detailed review in the field of Handwritten Character Recognition.

Feature Extraction Technique for Handwritten Character Recognition Using Geometric-Based Artificial Neural Network

International Journal of Advance Engineering and Research Development, 2016

Automatichandwritten characters recognition is a problem, which is currently gathering a lot of attention. The ability of an efficient processing small handwriting samples, such as those found on cheques and envelopes, is one of the significant driving forces behind this current research. This paper describes a geometry based technique for feature extraction which applies to the segmentation-based word recognition systems.In this methodology, an artificial neural network is trained to identify resemblance and patterns among different handwriting character dataset training samples and user-entered characters. The proposed system extracts the geometric features of character and thereby, forming a characterskeleton.The system generates feature vectors as outputs which are used to train a pattern recognition engine based on Neural Networks which makes the system benchmarked. We acquired an accuracy of 95.2% working on a set of 108 features. The Feature-Extraction methods described in this paper have performed well in classification when fed to the neural network, and pre-processing of the image using edge detection method and normalization technique are the ideal choice for degraded noisy images.

Online Handwritten Character Recognition for a Personal Computer System

IEEE Transactions on Consumer Electronics, 1982

An online handwritten character recognition algorithm, suitable for the home use computer, was developed. A character, written on a digitizing tablet, is expressed as a directional angle sequence. In order to recognize a quickly written multi-stroke character, the character pattern is converted into a single interconnected stroke pattern. The recognition is carried out using dynamic programming based pattern matching technique. The algorithm was experimentally implemented in a personal computer system, and proved to have excellent realtime operation and a high recognition accuracy, as high as 99.5%.

Improving Various Offline Techniques used for Handwritten Character Recognition : A Review

International Journal of Computer Applications, 2012

Handwritten character recognition is always an advanced area of research in the field of image processing and pattern recognition and there is a large demand for OCR on offline hand written documents. Even though, sufficient studies have performed from history to this era, paper describes the techniques for converting textual content from a paper document into machine readable form. The computer actually recognizes the characters in the document through a revolutionizing technique called Optical Character Recognition (OCR). There are many paper deals with issues such as hand-printed character and cursive handwritten word recognition which describes recent achievements, difficulties, successes and challenges in all aspects of handwriting recognition. Their many papers present a new approach which improves current handwriting recognition systems. Some experimental results are included. Selection of a relevant feature extraction method is probably the single most important factor in achieving high recognition performance with much better accuracy in character recognition systemsn this paper, we describe the formatting guidelines for IJCA Journal Submission.