A WAVE APPROACH TO PATTERN RECOGNITION (WITH APPLICATION TO OPTICAL CHARACTER RECOGNITION)

1994 ◽  
Vol 04 (01) ◽  
pp. 193-207 ◽  
Author(s):  
VADIM BIKTASHEV ◽  
VALENTIN KRINSKY ◽  
HERMANN HAKEN
Keyword(s):  
Pattern Recognition ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Dissipative Structures ◽  
Binary Images ◽  
Optical Character ◽  
Nonlinear Version ◽  
Medium Quality ◽  
Scalar Products ◽  
Machine Reading

The possibility of using nonlinear media as a highly parallel computation tool is discussed, specifically for image classification and recognition. Some approaches of this type are known, that are based on stationary dissipative structures which can “measure” scalar products of images. In this paper, we exploit the analogy between binary images and point sets, and use the Hausdorff metrics for comparing the images. It does not require the measure at all, and is based only on the metrics of the space whose subsets we consider. In addition to Hausdorff distance, we suggest a new “nonlinear” version of this distance for comparison of images, called “autowave” distance. This distance can be calculated very easily and yields some additional advantages for pattern recognition (e.g. noise tolerance). The method was illustrated for the problem of machine reading (Optical Character Recognition). It was compared with some famous OCR programs for PC. On a medium quality xerocopy of a journal page, in the same conditions of learning and recognition, the autowave approach resulted in much fewer mistakes. The method can be realized using only one chip with simple uniform connection of the elements. In this case, it yields an increase in computation speed of several orders of magnitude.

scholarly journals Demystification of Bilingual Optical Character Recognition System for Devanagari and English Scripts

2019 ◽  
Vol 9 (1) ◽  
pp. 5180-5185
Keyword(s):  
Pattern Recognition ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Recognition System ◽  
Research Paper ◽  
New Approach ◽  
Optical Character ◽  
New Ideas

Research is deliberately going on in the field of pattern recognition. New ideas are developed and implemented in this field throughout the globe. Optical Character Recognition (OCR) is one of the inseparable applications of Pattern Recognition. Though extensive research is already reported in this field, but multilingual Optical Character Recognition is the most challenging aspect which is still, the need of the hour. Myriads of researchers are digging the information to gather the best solutions for the recognition purpose. In this research paper, we are purposing the steps for the recognition of Devanagari and English scripts simultaneously occurring in the documents. A new approach of segmentation and splitting the characters of both the scripts is also introduced for the benefits of researchers. Most commonly in the documents containing English and Devanagari scripts, English characters are already separated, the challenge is to separate the Devanagari characters. Algorithm to implement the challenging aspect to segment the Devanagari and Roman scripts simultaneously is also implemented in the present paper.

scholarly journals License Plate Recognition for Vehicle Surveillance

2020 ◽  
pp. 134-138
Keyword(s):  
Image Processing ◽  
Pattern Recognition ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Recognition System ◽  
Traffic Monitoring ◽  
Color Contrast ◽  
License Plate ◽  
License Plate Recognition ◽  
Optical Character

License plate recognition system plays very important role in various security aspects which includes entry monitoring of a particular vehicle in commercial complex, traffic monitoring , identification of threats and many more. In past few years many different methods has been adopted for license plate recognition system but still there is little more chance to work on real time difficulties which come across while license plate recognition like speed of vehicle, angle of license plate in picture, background of picture or color contrast of image, reflection on the license plate and so on. The combination of object detection, image processing, and pattern recognition are used to fulfill this application. In the proposed architecture , system will capture a small video and using Google's OCR(Optical Character Recognition) system will recognize license number, if that number get found in database gate will get open with the help of Arduino Uno.

Fast Recognition of Noisy Digits

1993 ◽  
Vol 5 (6) ◽  
pp. 885-892 ◽  
Author(s):  
Jeffrey N. Kidder ◽  
Daniel Seligson
Keyword(s):  
Character Recognition ◽  
Optical Character Recognition ◽  
High Speed ◽  
Rejection Rate ◽  
Feedforward Network ◽  
Software Simulation ◽  
Recognition Error ◽  
Binary Images ◽  
Optical Character ◽  
Fast Recognition

We describe a hardware solution to a high-speed optical character recognition (OCR) problem. Noisy 15 × 10 binary images of machine written digits were processed and applied as input to Intel's Electrically Trainable Analog Neural Network (ETANN). In software simulation, we trained an 80 × 54 × 10 feedforward network using a modified version of backprop. We then downloaded the synaptic weights of the trained network to ETANN and tweaked them to account for differences between the simulation and the chip itself. The best recognition error rate was 0.9% in hardware with a 3.7% rejection rate on a 1000-character test set.

scholarly journals Line Segmentation Challenges in Tamil Language Palm Leaf Manuscripts

2019 ◽  
Vol 9 (1) ◽  
pp. 2363-2367
Keyword(s):  
Character Recognition ◽  
Optical Character Recognition ◽  
Character Segmentation ◽  
Binary Images ◽  
Optical Character ◽  
Colour Image ◽  
Segmentation Methods ◽  
Segmentation Algorithms ◽  
Line Segmentation ◽  
Palm Leaf

The process of an Optical Character Recognition (OCR) for ancient hand written documents or palm leaf manuscripts is done by means of four phases. The four phases are ‘line segmentation’, ‘word segmentation’, ‘character segmentation’, and ‘character recognition’. The colour image of palm leaf manuscripts are changed into binary images by using various pre-processing methods. The first phase of an OCR might break through the hurdles of touching lines and overlapping lines. The character recognition becomes futile when the line segmentation is erroneous. In Tamil language palm leaf manuscript recognition, there are only a handful of line segmentation methods. Moreover, the available methods are not viable to meet the required standards. This article is proposed to fill the lacuna in terms of the methods necessary for line segmentation in Tamil language document analysis. The method proposed compares its efficiency with the line segmentation algorithms work on binary images such as the Adaptive Partial Projection (APP) and A* Path Planning (A*PP). The tools and criteria of evaluation metrics are measured from ICDAR 2013 Handwriting Segmentation Contest.

Intriguing Aspects of Oriental Languages

1998 ◽  
Vol 12 (01) ◽  
pp. 5-29 ◽  
Author(s):  
Ching Y. Suen ◽  
Shunji Mori ◽  
Hae-Chang Rim ◽  
Patrick S. P. Wang
Keyword(s):  
Pattern Recognition ◽  
Computational Linguistics ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Contextual Information ◽  
Future Trends ◽  
Pattern Recognition Techniques ◽  
Optical Character

This paper includes a description of 3 affiliated oriental languages: Chinese, Japanese, and Korean. It includes a description of the origins of these 3 languages and the inter-relationship among them. Drawn from the viewpoints of several experienced researchers in the field of OCR (Optical Character Recognition) and computational linguistics, it attempts to bring out the intriguing aspects of these 3 ideographic languages, including the formation and composition of pictograms, special features, learning, understanding, contextual information, and recognition of characters and words, and their relations to poetic expressions and pattern recognition techniques. Numerous references are given and comments on future trends are also presented.

scholarly journals Text Recognition from Document Images

2019 ◽  
pp. 75-79
Author(s):  
Revanth Yenugudhati ◽  
Suresh Babu Papanaboina ◽  
Suryatej Vasireddy ◽  
Yaswanth Seelam
Keyword(s):  
Character Recognition ◽  
Optical Character Recognition ◽  
Reading Skills ◽  
Text Recognition ◽  
Current Investigation ◽  
Document Images ◽  
Optical Character ◽  
Specific Protocol ◽  
Technological Advances ◽  
Machine Reading

The objective is the development of effective reading skills in machines. After reading the text and comprehending the meaning, it would know itself and according to the program, it would implement the instructions. The current investigation presents an algorithm and software which detects, recognizes text and character with specific protocol in a image and programs itself according to the text. Technological advances in image processing have acquainted us with character recognition and many such related technologies, which have proved to be a milestone. However, even years after the invention of these technologies we have not been able to achieve a technology by which machine can read, interpret and act according to the instructions and even update their database if required. Here’s an attempt to make this reality. Machine replication of human functions, like reading, is a long-awaited dream. However, over the last five decades, machine reading has transformed from a dream to reality. Text detection and character recognition known as Optical Character Recognition (OCR) has become one of the most successful applications of technology in the field of pattern recognition and artificial intelligence. Numerous commercial systems for OCR exist for a variety of applications

scholarly journals Script Identification for Devanagari and Gurumukhi using OCR

2021 ◽  
Vol 10 (9) ◽  
pp. 12-22
Author(s):  
Shubhankar Sharma ◽  
Vatsala Arora
Keyword(s):  
Pattern Recognition ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Document Images ◽  
Script Identification ◽  
Pattern Recognition Techniques ◽  
Recognition Process ◽  
Optical Character ◽  
Handwritten Text ◽  
Handwritten Document

The study of character research is an active area for research as it pertains a lot of challenges. Various pattern recognition techniques are being used every day. As there are so many writing styles available, development of OCR (Optical Character Recognition) for handwritten text is difficult. Therefore, several measures have to be taken to improve the recognition process so that the burden of computation can be decreased and the accuracy for pattern recognition can be increased. The main objective of this review was to recognize and analyze handwritten document images. In this paper, we present a scheme to identify different Indian scripts like Devanagari and Gurumukhi.

Optical Character Recognition of Postmark Date Based on Machine Vision

2012 ◽  
Vol 424-425 ◽  
pp. 1107-1111
Author(s):  
Fu Cheng You ◽  
Ying Jie Liu
Keyword(s):  
Image Processing ◽  
Pattern Recognition ◽  
Machine Vision ◽  
Information Management ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Binary Image ◽  
Support Vector ◽  
Threshold Method ◽  
Optical Character

For the purpose of information management on postmark according to the date, the paper put forward a method of postmark date recognition based on machine vision, which could meet the demands of personal postmark collectors. On the basis of the relative theories of machine vision, image processing and pattern recognition, the overall process is introduced in the paper from postmark image acquisition to date recognition. Firstly, threshold method is used to generate binary image from smoothed postmark image. So region of date numbers could be extracted from binary image according to different region features. Then regions of date numbers which are connected or broken could be processed through mathematical morphology of binary image. Individual regions of date numbers are obtained for recognition. Finally, classification and pattern recognition based on support vector machine make date numbers classified and date recognition is implemented correctly

A Structured Method for the Recognition of Complex Historical Tables

1997 ◽  
Vol 9 (1-3) ◽  
pp. 58-77
Author(s):  
Vitaly Kliatskine ◽  
Eugene Shchepin ◽  
Gunnar Thorvaldsen ◽  
Konstantin Zingerman ◽  
Valery Lazarev
Keyword(s):  
Nineteenth Century ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Complex Structure ◽  
Source Material ◽  
Historical Sources ◽  
Tax Assessment ◽  
Optical Character ◽  
Algorithmic Model ◽  
Machine Readable

In principle, printed source material should be made machine-readable with systems for Optical Character Recognition, rather than being typed once more. Offthe-shelf commercial OCR programs tend, however, to be inadequate for lists with a complex layout. The tax assessment lists that assess most nineteenth century farms in Norway, constitute one example among a series of valuable sources which can only be interpreted successfully with specially designed OCR software. This paper considers the problems involved in the recognition of material with a complex table structure, outlining a new algorithmic model based on ‘linked hierarchies’. Within the scope of this model, a variety of tables and layouts can be described and recognized. The ‘linked hierarchies’ model has been implemented in the ‘CRIPT’ OCR software system, which successfully reads tables with a complex structure from several different historical sources.

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