Optical Character Recognition (OCR) is one of the challenging areas in the domain of image processing, where the handwritten or printed characters are digitized by using an optical scanner. The image is then analyzed broadly by two methods – (i) matrix space analysis method and (ii) feature space analysis method. Matrix space analysis method takes more memory space and time, compared to feature space analysis. However, it works fine for the scripts in which the strokes are prominent, e.g. English numeric scripts. On the other hand, the feature analysis method is useful where the scripts are complex and having more similarity between the letters in its writing style. Hence, the feature analysis approach is more useful to many of the regional languages. In this paper, we have used the Ant-miner algorithm (AMA) for offline OCR of hand written Oriya scripts, popularly known as Utkal lipi. The AMA is a rule-based approach. The rules are incrementally tuned during the training. The Oriya language contains more than 50 distinct characters i.e. 12 Swara-varnas (i.e., vowels) and 38 Byanjan-varnas (i.e., consonants) and their composite characters. In this work, for the analysis, we define three types of ‘block’s as per the writing styles of the scripts. AMA is then tested with four characters from each ‘block’. Finally, a character recognition tool has been developed using Matlab for observation and validation. General terms: Ant miner algorithm, Optical character recognition (OCR), Oriya character pattern matching

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Optical Character Recognition using Ant Miner Algorithm: A Case Study on Oriya Character Recognition

Hand written character recognition is the phenomena of enabling a machine to automatically recognize the characters or scripts written in user language. Optical character recognition has become one of the most successful applicants of technology in the field of pattern recognition and artificial intelligence in this project a scanned image is translated into machine established text by means of using optical character recognition. Here a hand written English cursive word is scanned and this image is fed into the computer in which it is recognized using neural network and converted into the same work in equivalent printed characters. A near combination algorithm are developed for each process for the purpose of segmentation and zoning phenomena to achieve better accuracy and to overcome all the drawbacks found in all other available OCR algorithm.

http://ieeexplore.ieee.org/iel7/7731602/7754737/07755617.pdf

Enhancement of segmentation and zoning to improve the accuracy of handwritten character recognition

Optical Character Recognition (OCR) is a type of computer software designed to translate images of handwritten or typewritten text (usually captured by a scanner or a camera) into machineeditable text by recognizing characters at high speeds one at a time. OCR began as a field of research in pattern recognition, artificial intelligence and machine vision. It is becoming more and more important in the modern world according to economic reasons and business requirements. It helps humans ease their jobs and solve more complex problems by eliminating the time-consuming spent by human operators to re-type the documents and reduce error-prone processes. The presence of any type of noise or a combination of them can severely degrade the performance of OCR system. Though, a number of preprocessing techniques are considered in the present work in order to improve the obtained accuracy of the recognized text. An OCR system for 3185 training samples and 13650 testing samples is presented for multi-font English texts. Experiments have shown that wavelet features produce better recognition rates 96% than DCT features 92%. An improvement overall recognition rates (about 3%) are obtained after classifying characters according to the proportion of Height to Width feature to produce 99% for wavelet and 95% for DCT.

Optical character recognition (ocr) system for multi- font english texts using dct & wavelet transform

From past decades, many research works have been carried out for identifying characters from images in Gujarati language. However, a few solutions are available for segmentation of words and characters in Gujarati language from a text file. In this paper, we propose a novel algorithm which considers input from a text file containing Gujarati script and segments words and characters. The proposed algorithm has been validated with 10 numbers written in words, each with varying length, implemented using MATLAB. The screenshots are attached with this paper, which shows segmentation of words and characters. This system is considered as a subsystem of a much bigger system, where Gujarati text is to be converted into to its equivalent speech. Without proper segmentation of words and characters, it is not possible to achieve the intended task. Prior to this, existing solutions and tools are analysed and the summary is presented in the literature survey. Based on the limitations found through the survey, the proposed research work is designed. An experiment is also carried out and discussed in detail in this paper along with the results achieved. At end, conclusion and possible future enhancements are also presented.

Implementation of Words and Characters Segmentation of Gujarati Script Using MATLAB

OCR is the acronym for Optical Character Recognition. This technology allows a machine to automatically recognize characters through as optical mechanism. Human Beings “recognize” many objects in this manner; our eyes are the “optical mechanism.” But while the brain “sees” the input, the ability to comprehend these signals varies in each person according to many factors. In same manner “characters” which are nothing but the images made by the different combinations of lines and curves are also optically recognized by our brain. By reviewing these variables, the challenges faced by the technologist developing an OCR system. Character recognition techniques help in recognizing the characters written on paper documents and converting it in digital form. So Character recognition is gaining interest and importance in the modern world. While the area of character recognition is vast we focus on the fundamentals of character recognition, available techniques and emphasis on more recently used technique, neural networks. The paper throws light on, one of the application of Neural Network (NN) i.e. Character Recognition.

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Vijay Kumar

Papers

Character Recognition: A Neural Network Approach