Printed Character Recognition Research Articles

Reconstruction of Oriya Alphabets Using Zernike Moments

There has been a significant amount of research in pattern recognition in different aspects of printed character based user interfaces including interactive design tools, ink beautification and printed character recognition. Optical Character recognition (OCR) systems have been effectively developed for the recognition of printed characters of non-Indian languages. Efforts are on the way for the development of efficient OCR system for Indian language especially for ORIYA. I present in this paper the reconstruction of the basic characters (vowels and consonants) in Oriya text, which can handle different font sizes and font types. Hu‟s seven moments and Zernike moments have been progressively used to extract the features of ORIYA characters. When I reconstruct by taking the extracted features, due to certain difficulties in the Hu‟s moments I can use the Zernike moments. The methodology can be extended for recognition of the ORIYA conjuncts too.

Status of pattern recognition with wavelet analysis

Pattern recognition has become one of the fastest growing research topics in the fields of computer science and electrical and electronic engineering in the recent years. Advanced research and development in pattern recognition have found numerous applications in such areas as artificial intelligence, information security, biometrics, military science and technology, finance and economics, weather forecast, image processing, communication, biomedical engineering, document processing, robot vision, transportation, and endless other areas, with many encouraging results. The achievement of pattern recognition is most likely to benefit from some new developments of theoretical mathematics including wavelet analysis. This paper aims at a brief survey of pattern recognition with the wavelet theory. It contains the following respects: analysis and detection of singularities with wavelets; wavelet descriptors for shapes of the objects; invariant representation of patterns; handwritten and printed character recognition; texture analysis and classification; image indexing and retrieval; classification and clustering; document analysis with wavelets; iris pattern recognition; face recognition using wavelet transform; hand gestures classification; character processing with B-spline wavelet transform; wavelet-based image fusion, and others.

Hopfield-multilayer-perceptron serial combination for accurate degraded printed character recognition

Degraded printed character recognition is a hard and ever-present problem in optical character recognition. Previous work has explored serial combination of multilayer perceptron (MLP) and autoassociators networks for printed character recognition. The MLP is used as a first step classifier for its discrimination capability and is not very well suited for rejection. On the other hand, the autoassociator used as a second step classifier is more appropriate when a very small error and rejection are required. Unfortunately, this better behavior with respect to rejection is either paid in terms of rejection error or in terms of computational complexity, particularly when the number of classes is high. In this paper, we propose a serial combination of the Hopfield and MLP networks in order to achieve accurate recognition of degraded printed characters. We introduce a relative distance and use it as a quality measurement of the degraded character, which makes the Hopfield-based classifier very powerful and very well-suited for rejection. This relative distance is compared to a rejection threshold in order to accept or reject the incoming degraded character by the Hopfield model used as a first classifier. Due to its discrimination capability, the MLP network is used as a second classifier to avoid rejection error and to diminish computational complexity. The proposed method is devoted to solving the problem of recognition of single font characters collected from poor quality bank checks. We report experimental results from a comparison of three neural architectures: the Hopfield network, the MLP-based classifier, and the proposed combined architecture. The proposed method is also compared to five other recognition systems. It is shown that the proposed architecture exhibits the best performance, with no significant increase in the computational burden. In this paper, we propose also a bank check processing procedure for account check number (ACN) detection, localization, and character retrieval...

KHMER PRINTED CHARACTER RECOGNITION BY USING WAVELET DESCRIPTORS

In Khmer printed characters, same character has various shapes according to the fonts and some characters are very similar in shape. In this paper we try to solve these problems, and propose a method of Khmer printed character recognition by using Wavelet Descriptors. In the recognition, firstly the Khmer printed character images are converted to skeleton forms, then skeletons of Khmer character are converted to temporal domain. The templates are obtained by wavelet coefficients from the character training set. To match the input characters with templates, the character recognition method using deformable wavelet descriptor is adapted by using fixed template and Euclidean distance classifier for matching. The smallest distance is the recognition result of the proposed method. As a result, the deformation can be skipped because it might get low recognition rate of similar characters. The experiment consists of two parts. The first part is to evaluate the overall recognition rate of input characters with three different sizes (22-point, 18-point and 12-point) from 10 different fonts of Khmer printed character. Twenty styles of characters are used as the training set. The results show 92.85, 91.66, and 89.27 percent for 22-point, 18-point, and 12-point respectively. The second part is to specifically evaluate the system, testing with one document that has 21 pages of Khmer printed character with different resolutions from a scanner and facsimile (fax). The document is initially printed with 300 dpi (dots per inch), then scanned with three different resolutions, 600 dpi, 300 dpi and 150 dpi. The document that received from fax machine is scanned by 300 dpi. The results show 92.99, 88.61, and 80.05 percent recognition rate for 300, 150 dpi resolutions, and input from fax respectively.

A segmentation-free approach to recognise printed Sinhala script using linear symmetry

In this paper, a novel approach for printed character recognition using linear symmetry is proposed. When the conventional character recognition methods such as the artificial neural network based techniques are used to recognise Brahmi Sinhala script, segmentation of modified characters into modifier symbols and basic characters is a necessity but a complex issue. The large size of the character set makes the whole recognition process even more complex. In contrast, in the proposed method, the orientation features are effectively used to recognise characters directly using a standard alphabet as the basis without the need for segmentation into basic components. The edge detection algorithm using linear symmetry recognises vertical modifiers. The linear symmetry principle is also used to determine the skew angle. Experiments with the aim for an optical character recognition system for the printed Sinhala script show favourable results.

Enhancing multiple expert decision combination strategies through exploitation of a priori information sources

In recent years the concept of combining multiple experts in a unified framework to generate a combined decision based on individual decisions delivered by the cooperating experts has been exploited in solving the problem of handwritten and machine printed character recognition. The level of performance achieved in terms of the absolute recognition performance and increased confidences associated with these decisions is very encouraging. However, the underlying philosophy behind this success is still not completely understood. The authors analyse the problem of decision combination of multiple experts from a completely different perspective. It is demonstrated that the success or failure of the decision combination strategy largely depends on the extent to which the various possible sources of information are exploited in designing the decision combination framework. Seven different multiple expert decision combination strategies are evaluated in terms of this information management issue. It is demonstrated that it is possible to treat the comparative evaluation of the multiple expert decision combination approaches based on their capability for exploiting diverse information extracted from the various sources as a yardstick in estimating the level of performance that is achievable from these combined configurations.

HYBRID LOCATION-CONTENT ADDRESSABLE MEMORY WITH ITS APPLICATION TO CHARACTER RECOGNITION

This paper reviews the Hybrid Location-Content Addressable Memories (HyLCAMs) and applies them to the printed character recognition problem in order to illustrate their performance in real world problems. Three indirect code generation methods which are the key of the HyLCAM encoding is described to determine a set of proper intermediate states that are linearly separable with inputs. Rejection mechanisms based on code mismatch and rejection region of activation functions are introduced. Computer simulations are performed in order to compare generalization abilities and performance on the rejection mechanism of the HyLCAM models with those of the multilayer perceptrons.

Combination of HMMs for the representation of printed characters in noisy document images

Many methods of printed character recognition have been proposed to-date, but although performance figures are usually stated for a particular set of fonts or size of text, it is rarely clear under what conditions of noise the measurements were taken. Baird has suggested a model of Document Imaging Defects, which enables authors to compare results against an emerging standard where one figure can be quoted to quantify the level of noise present in the document image. In this paper a novel method is proposed for the recognition of printed characters, and its extension to the segmentation and recognition of noisy printed words is outlined. The method is based on the representation of the shape of a character by two Hidden Markov Models. Recognition is achieved by scoring these models against the test pattern and combining the results. The method has been evaluated using Baird's noise model, producing a peak performance of 99.5% on the test set in the presence of near-minimal noise. The method generalizes to recognize characters with noise levels greater than those included in the training set, and an investigation of the top- k performance suggests that much of the effect of noise on the recognition performance on images of natural language text could be overcome using a word recognizer employing shallow contextual knowledge.

Application of fuzzy theory to handwritten character recognition

AbstractThere has been much research in recent decades on character recognition methods, and some methods have already been put into practical use. There are many unresolved problems, however, with respect to handwritten character recognition as composed with printed character recognition. The authors considered discriminant functions, which constitute the most important part of a character recognition method. As a result of considering problems of conventional statistical discriminant functions, the authors propose applying the fuzzy theory to discriminant functions. The so‐called fuzzy discriminant function is capable of representing a data distribution in a more flexible manner because it consists of membership functions on the principal axes of learning samples.The authors conducted recognition experiments for handwritten characters with two types of membership functions. In one type the membership values are directly tuned based on human experiences; in the other they are derived from histograms or statistical data. With the former membership function, the recognition rate of 99.0 percent is achieved for [numeric] characters from the handwritten alphanumeric data base ETL6, and with the latter, the rate of 96.0 percent for [hiragana] characters from handwritten educational [kanji] data base ETL8. This result proves the effectiveness of the fuzzy discriminant function. It also indicates that a dynamic combination of human experiences and statistical techniques is a key to practical systems.

Segmentation of merged characters by neural networks and shortest path

A major problem with a neural network-based approach to printed character recognition is the segmentation of merged characters. A hybrid method is proposed which combines a neural network-based deferred segmentation scheme with conventional immediate segmentation techniques. In the deferred segmentation, a neural network is employed to distinguish single characters from composites. To find a proper vertical cut that separates a composite, a shortest-path algorithm seeking minimal-penalty curved cuts is used. Integrating those components with a multiresolution neural network OCR and an efficient spelling checker, the resulting system significantly improves its ability to read omnifont document text.

Hidden Markov models for character recognition

A hierarchical system for character recognition with hidden Markov model knowledge sources which solve both the context sensitivity problem and the character instantiation problem is presented. The system achieves 97-99% accuracy using a two-level architecture and has been implemented using a systolic array, thus permitting real-time (1 ms per character) multifont and multisize printed character recognition as well as handwriting recognition.

OPTICAL CHARACTER RECOGNITION — A SURVEY

In order to highlight the interesting problems and actual results on the state of the art in optical character recognition (OCR), this paper describes and compares preprocessing, feature extraction and postprocessing techniques for commercial reading machines. Problems related to handwritten and printed character recognition are pointed out, and the functions and operations of the major components of an OCR system are described. Historical background on the development of character recognition is briefly given and the working of an optical scanner is explained. The specifications of several recognition systems that are commercially available are reported and compared.

Printed character recognition by self-organizing Boolean networks: [formula omitted], IBM European Center for Scientific and Engineering Computing, Via Giorgione 159, Roma, Italy 00147; [formula omitted], IBM Scientific Center, Via Giorgione 159, Roma, Italy 00147; [formula omitted], IBM, Direzione Ricerca Scientifica e Tecnologica, Via Giorgione 159, Roma, Italy 00147

Printed character recognition by self-organizing Boolean networks: [formula omitted], IBM European Center for Scientific and Engineering Computing, Via Giorgione 159, Roma, Italy 00147; [formula omitted], IBM Scientific Center, Via Giorgione 159, Roma, Italy 00147; [formula omitted], IBM, Direzione Ricerca Scientifica e Tecnologica, Via Giorgione 159, Roma, Italy 00147