A Literature Survey on Handwritten Character Recognition (original) (raw)

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.

Handwritten Character Recognition – A Review

In the field of pattern recognition, HCR is one of the most intricate and tricky area. Plenty of works were proposed for foreign languages but a few works exists for south Indian languages due to the complex shape and varying writing styles of individuals. This paper introduces a review of offline and online recognition of different natural languages. HCR is an optical character recognition, which convert the textual document in to machine readable format. To attain 99.9 % accuracy in the field of HCR is very difficult. The efficiency of HCR depend the features extracted and the classifier used.

A Survey on Handwritten Character Recognition (HCR) Techniques for English Alphabets

Nowadays Hand written Character Recognition (HCR) is major remarkable and difficult research domain in the area of Image processing. Recognition of Handwritten English alphabets have been broadly studied in the previous years. Presently various recognition methodologies are in well-known utilized for recognition of handwritten English alphabets (character). Application domain of HCR is digital document processing such as mining information from data entry, cheque, applications for loans, credit cards, tax, health insurance forms etc. During this survey we present an outline of current research work conducted for recognition of handwritten English alphabets. In Handwritten manuscript there is no restriction on the writing technique. Handwritten alphabets are complicated to recognize because of miscellaneous human handwriting technique, difference in size and shape of letters, angle. A variety of recognition methodologies for handwritten English alphabets are conferred here alongside ...

A Review of Handwritten Character Recognition

International Journal of Computer Applications

The aim behind Optical Character Recognition is to create human like perception and character identification by artificial systems. A lot of work has been done for printed and handwritten character recognition for many languages across the world. Even for many Indian languages, a good amount of work is done, but it could not get that accuracy as English, Germen etc. languages because of its complexities. In this paper various techniques for Handwritten Character Recognition (HCR) are reviewed and analyzed.

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.

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

IEEE Access

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.

Optical Recognition of Handwritten Text

The project focus on the creation of OCR software for the off-line recognition of handwriting. OCR programs can recognize printed text with nearly perfect accuracy. The recognition of handwriting is harder due to the many different styles and inconsistent nature of handwriting. Handwritten text recognition (HTR) is an open field of research and a relevant problem that helps automatically process historical documents. In recent years great advances in deep learning and computer vision have allowed improvements on document and image processing including HTR. Handwritten text recognition plays an important role in the processing of vital information. Processing of digital files is cheaper than processing traditional paper files even though a lot of information is available on paper. The aim of an OCR software is to convert handwritten text into machine readable formats. Despite such advances in this field, little has been done to produce open-source projects that address this problem as well as methods that utilize graphical process units (GPUs) to speed up the training phase.

A Research on Handwritten Text Recognition

INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT

Over the years we have started accumulating handwritten documents, like pdfs, doc files and numerous other formats for reading, writing and studying. Often we come across situations where we need to utilize the text of those documents. Manually transcribing large amounts of handwritten data is an arduous process that’s bound to be fraught with errors. Automated handwriting recognition can drastically cut down on the time required to transcribe large volumes of text, and also serve as a framework for developing future applications of machine learning. Recognition can be offline or online and both can be implemented in applications to progressively learn based on the user’s feedback while performing offline learning on data in parallel. Some recognition systems identify strokes, others apply recognition on a single character or entire words. Some steps involved in the area(in no particular order): Image preprocessing, Segmentation, Classification & Recognition, Feature Extraction. Fin...

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.

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.