OCR

— Over the last five years optical character recognition approaches have been under gone an enormous number of changes. Many efforts have been done and a wide range of algorithms have been used in order to improve the performance of existing methods of OCR in many languages. This paper presents an overview of feature extraction methods for character recognition in different texts. The feature extraction stage is an important component of any recognition system. It is also very much dependent on the task, input, and recognition algorithm used. The feature extraction methods are discussed in terms of invariance properties and expected distortions and variability of the characters.

Vol. 19 No. 2 FEBRUARY 2021 International Journal of Computer Science and Information Security (IJCSIS), 2021

This paper provides a total overview of OCR. Optical character recognition is nothing but the ability of the computer to collect and decipher the handwritten inputs from documents, photos or any other devices. Over these many years, many researchers have been researching and paying attention on this topic and proposed many methods which can be solved. This research provides a historical view and the summarization of the research which done on this field.

International Journal of Advanced Trends in Computer Science and Engineering, 2020

The technology associated with character recognition has emerged as a vital technology within the era of the fourth historic period. Character recognition is developing as a core technology needed in various fields. Character recognition is performed by extracting characters from a picture and recognizing the extracted characters. Character recognition technology has been continuously developed. Recently, together with the event of the fourth historic period, character recognition technology has been used as a core technology in many places. This paper introduces the technology associated with character recognition and therefore the program for character recognition.

IEEE Transactions on Pattern Analysis and Machine Intelligence, 1996

Character segmentation has long been a critical area of the OCR process. The higher recognition rates for isolated characters vs. those obtained for words and connected character strings well illustrate this fact. A good part of recent progress in reading unconstrained printed and written text may be ascribed to more insightful handling of segmentation.

Proceedings of The IEEE, 1992

This p a~e r discusses character segmentution methods, a key technology for character recognition that determines the usability and upplicubiliiy of optical character readers. A pattermoriented segmentation method that leads to document structure analysis is presented. A first example of advanced character segmrritation is touching handwritten numeral segmentation. Connected pattern components are extracted instead of U pixel image, and spatial interrelations between components are measured to group them into meaningful character patterns. Stroke shapes are analyzed in the ca.se of touching ckuracters. A method of finding the touching positions can separatc about 95% of connecred niimimls correctly. Ambiguities are handled by multiple hypotheses arid Lwificution by rwognition. An extended form of pattern-oriented segmentation is also discussed by presenting another example of tabular form recognition. Document images of tabular forms are analyzed, and frumev in the tabular .structure can be atracted. By identihing semantic relationships between label frames and data frames, information on the form can be properly recognized. Advanced character segmentation with a document structure analysis capabilit) is becoming increasingly significurit in automating information extraction from iarioiis kind5 of documents.

In the running world, there is growing demand for the software systems to recognize characters in computer system when information is scanned through paper documents as we know that we have number of newspapers and books which are in printed format related to different subjects. These days there is a huge demand in " storing the information available in these paper documents in to a computer storage disk and then later reusing this information by searching process ". One simple way to store information in these paper documents in to computer system is to first scan the documents and then store them as IMAGES. But to reuse this information it is very difficult to read the individual contents and searching the contents form these documents line-by-line and word-byword. The reason for this difficulty is the font characteristics of the characters in paper documents are different to font of the characters in computer system. As a result, computer is unable to recognize the characters while reading them. This concept of storing the contents of paper documents in computer storage place and then reading and searching the content is called DOCUMENT PROCESSING. Sometimes in this document processing we need to process the information that is related to languages other than the English in the world. For this document processing we need a software system called CHARCATER RECOGNITION SYSTEM. This process is also called DOCUMENT IMAGE ANALYSIS (DIA).

2021

Optical Character Recognition (OCR), is that the process of conversion of image text or handwritten text into machine understandable form. Simply OCR means conversion of characters that is recognized and convert it into computer readable form. It is widely used as a kind of data entry from original paper data sources such as banking papers or consultation papers, whether passport documents, invoices, statement, receipts, card, mail or any number of printed records. It is a standard method of digitizing printed texts in order that they will be electronically edited, searched, and stored more compactly. OCR is the field of research in Pattern Recognition, Artificial Intelligence and Computer Vision. OCR is that the electronic translation of handwritten, type written or printed text into machine translated images. It is widely used to recognize and search text from documents or to publish the text on a website. This document represents review of Optical Character Recognition methods su...

Lecture Notes in Computer Science, 2015

Optical Character Recognition (OCR) is a very extensive branch of pattern recognition. The existence of super effective software designed for omnifont text recognition, capable of handling multiple languages, creates an impression that all problems in this field have already been solved. Indeed, focus of research in the OCR domain has constantly been shifting from offline, typewritten, Latin character recognition towards Asiatic alphabets, handwritten scripts and online process. Still, however, it is difficult to come across an elaboration which would not only cover the topic of numerous feature extraction methods for printed, Latin derived, isolated characters conceptually, but which would also attempt to implement, compare and optimize them in an experimental way. This paper aims at closing this gap by thoroughly examining the performance of several statistical methods with respect to their recognition rate and time efficiency.

This paper presents detailed review in the field of Optical Character Recognition. Various techniques are determine that have been proposed to realize the center of character recognition in an optical character recognition system. Even though, sufficient studies and papers are describes the techniques for converting textual content from a paper document into machine readable form. Optical character recognition is a process where the computer understands automatically the image of handwritten script and transfer into classify character. This material use as a guide and update for readers working in the Character Recognition area. Selection of a relevant feature extraction method is probably the single most important factor in achieving high character recognition with much better accuracy in character recognition systems without any variation. Character recognition techniques associate a symbolic identity with the image of character. In a typical OCR systems input characters are digitized by an optical scanner. Each character is then located and segmented, and the resulting character image is fed into a pre-processor for noise reduction and normalization. Certain characteristics are the extracted from the character for classification. The feature extraction is critical and many different techniques exist, each having its strengths and weaknesses. After classification the identified characters are grouped to reconstruct the original symbol strings, and context may then be applied to detect and correct errors.

IJSRD, 2013

Nowadays character recognition has gained lot of attention in the field of pattern recognition due to its application in various fields. It is one of the most successful applications of automatic pattern recognition. Research in OCR is popular for its application potential in banks, post offices, office automation etc. HCR is useful in cheque processing in banks; almost all kind of form processing systems, handwritten postal address resolution and many more. This paper presents a simple and efficient approach for the implementation of OCR and translation of scanned images of printed text into machine-encoded text. It makes use of different image analysis phases followed by image detection via pre-processing and post-processing. This paper also describes scanning the entire document (same as the segmentation in our case) and recognizing individual characters from image irrespective of their position, size and various font styles and it deals with recognition of the symbols from English language, which is internationally accepted.

International Journal of Machine Learning and Computing, 2012

Optical Character Recognition or OCR is the electronic translation of handwritten, typewritten or printed text into machine translated images. It is widely used to recognize and search text from electronic documents or to publish the text on a website. The paper presents a survey of applications of OCR in different fields and further presents the experimentation for three important applications such as Captcha, Institutional Repository and Optical Music Character Recognition. We make use of an enhanced image segmentation algorithm based on histogram equalization using genetic algorithms for optical character recognition. The paper will act as a good literature survey for researchers starting to work in the field of optical character recognition.

Proc. Tamil Internet 2002 (TI 2002)

Algorithms for Intelligent Systems, 2021

Optical Character Recognition (OCR) is a technology that provides a full alphanumeric recognition of printed or handwritten characters. Optical Character Recognition is one of the most interesting and challenging research areas in the field of Image processing. Image Acquisition, Pre-processing, Segmentation, Feature Extraction and Classification are stages of OCR. In this paper, how character patterns are identified in the classification stage by different algorithms is presented. Template Matching Algorithm, statistical Algorithm, Structural Algorithm, Neural Network Algorithm and Support Vector Machine Algorithm are presented in this paper.