A Study on Optical Character Recognition Techniques

2017

Optical Character Recognition (OCR) has been a topic of interest for many years. It is defined as the process of digitizing a document image into its constituent characters. Despite decades of intense research, developing OCR with capabilities comparable to that of human still remains an open challenge. Due to this challenging nature, researchers from industry and academic circles have directed their attentions towards Optical Character Recognition. Over the last few years, the number of academic laboratories and companies involved in research on Character Recognition has increased dramatically. This research aims at summarizing the research so far done in the field of OCR. It provides an overview of different aspects of OCR and discusses corresponding proposals aimed at resolving issues of OCR.

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.

2000

The survey of today's state of tools for optical text recognition is given in this scientific paper. Tools for processing handwritten symbols still did not enter in wide usage except in some specific cases such as hand-held computer. In the context of this scientific paper, given solutions were used in program "Handwritten Symbol Recognition". Today, on the other hand, tools for printed text recognition are already in wide usage. In the context of this scientific paper, tests of speed and accuracy of the recognition had been carried out for few today's popular commercial tools.

This paper presents a literature review on English OCR techniques. English OCR system is compulsory to convert numerous published books of English into editable computer text files. Latest research in this area has been able to grown some new methodologies to overcome the complexity of English writing style. Still these algorithms have not been tested for complete characters of English Alphabet. Hence, a system is required which can handle all classes of English text and identify characters among these classes.

In this paper, we present a new neural network (NN) based method for optical character recognition (OCR) as well as handwritten character recognition (HCR). Experimental results show that our proposed method achieves increased accuracy in optical character recognition as well as handwritten character recognition. We present through an overview of existing handwritten character recognition techniques. All the algorithms describes more or less on their own. Handwritten character recognition is a very popular and computationally expensive task; we describe advanced approaches for handwritten character recognition. In the present work, we would like to compare the most important once out of the variety of advanced existing techniques, and we will systematize the techniques by their characteristic considerations. It leads to the behaviour of the algorithms reaches to the expected similarities.

—This paper describes two implementations in optical character recognition using template matching method and feature extraction method followed by support vector machine classification. With proper image preprocessing, the texts are segmented into isolated characters and the correlations between a single character and a given set of templates are computed to find the similarities and then identify the input character. In the second method, features extracted from the segmented characters are used to train the SVM classifiers, which are later, tested by a test set of handwritten digits.

International journal of computer applications, 2017

At present scenario, there is growing demand for the software system to recognize characters in a computer system when information is scanned through paper documents. This paper presents detailed review in the field of Optical Character Recognition. Various techniques are determined that have been proposed to realize the center of character recognition in an optical character recognition system. OCR (Optical Character Recognition) translates images of typewritten or handwritten characters into the electronically editable format and it preserves font properties. Different techniques for preprocessing and segmentation have been surveyed and discussed in this paper.

Optical Character Recognition”, abbreviated as OCR has become a very important aspect of life in today’s world especially in Computer vision applications, and also in the field of Robotics. Our basic objective is to develop an efficient approach which can recognize the input printed-text with a high level of accuracy so that its output can be utilized in our final goal which is an English-Text to multilingual Translator. In this paper the methodology adopted to implement OCR is mainly explained in detail along with emphasizing the necessity and advantages over the present technique. In this paper, an efficient approach for optical Character recognition of printed text has been analyzed. The problems associated with Character Recognition are also mentioned. A Graphical User Interface (GUI), in this paper represented for that purpose.

In the current paper we present a system of characters recognition by taking the photo of character with the identity of symbolic. In the proposed system we are going to make a scan in kind of optical for input character in order to be digitized. After that every character will be segmented and located and after that it will be obtained as a photo to be processed for normalization and even for reducing noise. After that it will be classified. Then from the obtained extraction we can find various techniques like weakness and strengths. Next step will be grouping the characters which identified in order to obtain the original string of symbols and we can apply the context in order to fix and detect false. The results show us that the system is working well and the recognition is really good. The system proposed in a program, developed in Matlab environment, which provides the ability to insert a character in an image. It is agree that making a machine to do what human can do is a dream, for example reading is one of the most important functions that humans are doing. However, this dream is becoming true day by day and researchers and working on this by many ways, where nowadays artificial intelligence is focusing on pattern recognition and in this field it is also focusing on the applications of character recognition and even many organizations and companies are designing systems for character recognition by many application and even that it is facing some challenges to make machines be able to read like humans and have the same capabilities. Recognizing characters is challenging some problems with the optical characters. Although, it is performed to be off line optical recognition for characters especially after completing the printing and writing, and to be online recognition to recognize characters as they have been drawn or written. Printed characters and even hand written characters could be recognized, but what we are always looking for is the performance where especially it is depending on the quality of files that been entered. Next step of challenging reviewed by many researchers is the online and the offline cursive writing. To get new ideas in the recognition of pattern, the classifying of characters could be tested, but where the experiments results are conducted on isolated characters, here the results are not necessary in case of immediately relevant to optical character recognition. Maybe more striking than the improvement of the accuracy and limit in methods of classification has been decreased in cost. The old devices of optical character recognition equipments were some optical hardware like the optical page reader of the company of IBM in order to read typed earning reports at the social security administration which cost more than two million dollars and some electronic and some high expensive scanners. Nowadays, the software of optical character recognition is often add on to scanner of desktop which is not costly. The main goal is to examine some details in examples of the false which committed by the proposed system. 2. PROPOSED SYSTEM The general technique is very simple to describe. The proposed optical character recognition system will contain some components and they are presented in figure 1. The install is illustrated, where to digitize the analog file by the optical scanner will be the first step in the system. After that the area which containing characters will be located and every symbol extracted by the process of segmentation. After that applying a preprocessing on the extracted symbols and then we are going to reduce the noise and eliminate it in order to make it easier the feature extraction to be prepared for the coming step. After that we are going to comparing the description of the classes of symbols which are gained by a phase of previous learning with the extracted features in order to find the identity of the symbol. Then to reconstruct the numbers and words of the original string we are going to use the contextual information.

Lecture Notes in Computer Science, 1998

The main objective of this paper is to introduce a novel method of feature extraction for character data and develop a neural network system for recognising different Latin characters, tn this paper we describe feature extraction, neural network development for character recognition and perform further neural network analysis on noisy image segments to explain the qualitative aspects of handwriting.

2018

Recent improvement in pattern recognition by many applications has been demanding , such as OCR, classification of Document, Data Mining etc. Use of OCR has vital role in Document scanners, character recognition, language recognition, security, authentication in Bank etc. OCR is classified into two types: online character recognition and offline character recognition system. Online OCR out beats offline OCR as characters are processed as it is written, this avoids initial stage of identifying the character .Offline OCR are further sub-divided into printed and handwritten OCR. In offline OCR are processed typically by scanning the typewritten /handwritten characters into binary or gray scale image to the recognition algorithm.

2014

The accurate recognition of Latin-script, typewritten text is now considered largely a solved problem. Typical accuracy rates exceed 99%, although certain applications demanding even higher accuracy require human review for errors. Other areas—including recognition of hand printing, cursive handwriting, and printed text in other scripts (especially those with a very large number of characters)--are still the subject of active research. Recognition of cursive text is an active area of research, with recognition rates even lower than that of hand-printed text. Higher rates of recognition of general cursive script will likely not be possible without the use of contextual or grammatical information. For example, recognizing entire words from a dictionary is easier than trying to parse individual characters from script. Reading the Amount line of a cheque (which is always a written-out number) is an example where using a smaller dictionary can increase recognition rates greatly. Knowledg...

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.

2020

1-2Student, Dept. of Computer Science and Engineering, MIT School of Engineering, MIT ADT University, Pune, Maharashtra, India ---------------------------------------------------------------------***---------------------------------------------------------------------Abstract Since olden times, the need for storing information in various ways has always been there. This was very useful until we felt the need to reuse this information again and again. In request to reuse these snippets of data, we had to read and search individual contents from different documents and then rewrite it again. Thus, there is an explicit need for automated softwares or programs in order to provide fast and accurate methods to revive the text from the longlasting images and documents.

International Journal for Scientific Research and Development, 2017

At present scenario, there is growing demand for the software system to recognize characters in a computer system when information is scanned through paper documents. This paper presents detailed review in the field of Optical Character Recognition. Various techniques are determined that have been proposed to realize the center of character recognition in an optical character recognition system. OCR (Optical Character Recognition) translates images of typewritten or handwritten characters into the electronically editable format and it preserves font properties. Where OCV (Optical Character Verification) is a hybrid approach of OCR and pattern matching. Different techniques for pre-processing and segmentation have been surveyed and discussed. Proposed methodology and the dataset are presented here. Intermediate results have shown in this paper.