Diagonal Based Feature Extraction for Handwritten Alphabets Recognition System using Neural Network
TL;DR: An off-line handwritten alphabetical character recognition system using multilayer feed forward neural network that will be suitable for converting handwritten documents into structural text form and recognizing handwritten names is described in the paper.
Abstract: An off-line handwritten alphabetical character recognition system using multilayer feed forward neural network is described in the paper. A new method, called, diagonal based feature extraction is introduced for extracting the features of the handwritten alphabets. Fifty data sets, each containing 26 alphabets written by various people, are used for training the neural network and 570 different handwritten alphabetical characters are used for testing. The proposed recognition system performs quite well yielding higher levels of recognition accuracy compared to the systems employing the conventional horizontal and vertical methods of feature extraction. This system will be suitable for converting handwritten documents into structural text form and recognizing handwritten names.
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08 Feb 2014
TL;DR: Various types of features, feature extraction techniques and explaining in what scenario, which features extraction technique, will be better are discussed and referred in case of character recognition application.
Abstract: Feature plays a very important role in the area of image processing. Before getting features, various image preprocessing techniques like binarization, thresholding, resizing, normalization etc. are applied on the sampled image. After that, feature extraction techniques are applied to get features that will be useful in classifying and recognition of images. Feature extraction techniques are helpful in various image processing applications e.g. character recognition. As features define the behavior of an image, they show its place in terms of storage taken, efficiency in classification and obviously in time consumption also. Here in this paper, we are going to discuss various types of features, feature extraction techniques and explaining in what scenario, which features extraction technique, will be better. Hereby in this paper, we are going to refer features and feature extraction methods in case of character recognition application.
357 citations
29 Jan 2016
TL;DR: A hybrid supervised classification technique that combines both low and high orders of learning is presented, which shows that the high level technique can realize classification according to the semantic meaning of the data.
Abstract: This book presents the features and advantages offered by complex networks in the machine learning domain. In the first part, an overview on complex networks and network-based machine learning is presented, offering necessary background material. In the second part, we describe in details some specific techniques based on complex networks for supervised, non-supervised, and semi-supervised learning. Particularly, a stochastic particle competition technique for both non-supervised and semi-supervised learning using a stochastic nonlinear dynamical system is described in details. Moreover, an analytical analysis is supplied, which enables one to predict the behavior of the proposed technique. In addition, data reliability issues are explored in semi-supervised learning. Such matter has practical importance and is not often found in the literature. With the goal of validating these techniques for solving real problems, simulations on broadly accepted databases are conducted. Still in this book, we present a hybrid supervised classification technique that combines both low and high orders of learning. The low level term can be implemented by any classification technique, while the high level term is realized by the extraction of features of the underlying network constructed from the input data. Thus, the former classifies the test instances by their physical features, while the latter measures the compliance of the test instances with the pattern formation of the data. We show that the high level technique can realize classification according to the semantic meaning of the data. This book intends to combine two widely studied research areas, machine learning and complex networks, which in turn will generate broad interests to scientific community, mainly to computer science and engineering areas.
86 citations
06 Apr 2016
TL;DR: The architecture, the steps involved, and the various proposed methodologies of offline and online character recognition along with their comparison and few applications are discussed.
Abstract: Handwritten character recognition has been one of the most fascinating research among the various researches in field of image processing. In Handwritten character recognition method the input is scanned from images, documents and real time devices like tablets, tabloids, digitizers etc. which are then interpreted into digital text. There are basically two approaches — Online Handwritten recognition which takes the input at run time and Offline Handwritten Recognition which works on scanned images. In this paper we have discussed the architecture, the steps involved, and the various proposed methodologies of offline and online character recognition along with their comparison and few applications.
47 citations
Cites background from "Diagonal Based Feature Extraction f..."
...Images of the sequence of character is decomposed into sub images of individual characters....
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TL;DR: In this research, a comparative experiment of 4 methods to identify plants using shape features was accomplished and Polar Fourier Transform gave best performance with 64% in accuracy and outperformed the other methods.
Abstract: Shape is an important aspects in recognizing plants. Several approaches have been introduced to identify objects, including plants. Combination of geometric features such as aspect ratio, compactness, and dispersion, or moments such as moment invariants were usually used toidentify plants. In this research, a comparative experiment of 4 methods to identify plants using shape features was accomplished. Two approaches have never been used in plants identification yet, Zernike moments and Polar Fourier Transform (PFT), were incorporated. The experimental comparison was done on 52 kinds of plants with various shapes. The result, PFT gave best performance with 64% in accuracy and outperformed the other methods.
46 citations
Journal Article•
TL;DR: The proposed hybrid feature extraction technique is a hybrid of structural, statistical and correlation features that results in a 157-variable feature vector for each character, which is adequate enough to uniquely represent and identify each character.
Abstract: In this paper, we describe hybrid feature extraction for offline handwritten character recognition. The proposed technique is a hybrid of structural, statistical and correlation features. In the first step, the proposed technique identifies the type and location of some elementary strokes in the character. The strokes to be looked for comprise horizontal, vertical, positive slant and negative slant lines–as we observe that the structure of any character can be approximated with the help of a combination of simple straight line strokes. The strokes are identified by correlating different segments of the character with the chosen elementary shapes. These normalized correlation values at different segments of the character give correlation features. For making feature extraction more robust, we add in the second step certain structural/statistical features to the correlation features. The added structural/statistical features are based on projections, profiles, invariant moments, endpoints and junction points. This enhanced, powerful combination of features results in a 157-variable feature vector for each character, which we find adequate enough to uniquely represent and identify each character. Prior, handwritten character recognition problem has not been addressed the way our proposed hybrid feature extraction technique deals with it. The extracted feature vector is used during the training phase for building a support vector machine (SVM) classifier. The trained SVM classifier is subsequently used during the testing phase for classifying unknown characters. Experiments were performed on handwritten digit characters and uppercase alphabets taken from different writers, without any constraint on writing style. The obtained results were compared with some related existing approaches. Owing to the proposed technique, the results obtained show higher efficiency regarding classifier accuracy, memory size and training time as compared to these other existing approaches.
43 citations
Additional excerpts
...In [12], recognition of handwritten alphabets using neural network and zoning based diagonal features is addressed....
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References
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TL;DR: This paper presents an overview of feature extraction methods for off-line recognition of segmented (isolated) characters in terms of invariance properties, reconstructability and expected distortions and variability of the characters.
1,376 citations
"Diagonal Based Feature Extraction f..." refers methods in this paper
...KEYWORDS Handwritten character recognition, Image processing, Feature extraction, feed forward neural networks....
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01 Jul 1992
TL;DR: Both template matching and structure analysis approaches to R&D are considered and it is noted that the two approaches are coming closer and tending to merge.
Abstract: Research and development of OCR systems are considered from a historical point of view. The historical development of commercial systems is included. Both template matching and structure analysis approaches to R&D are considered. It is noted that the two approaches are coming closer and tending to merge. Commercial products are divided into three generations, for each of which some representative OCR systems are chosen and described in some detail. Some comments are made on recent techniques applied to OCR, such as expert systems and neural networks, and some open problems are indicated. The authors' views and hopes regarding future trends are presented. >
892 citations
TL;DR: H holistic approaches that avoid segmentation by recognizing entire character strings as units are described, including methods that partition the input image into subimages, which are then classified.
Abstract: 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. This paper provides a review of these advances. The aim is to provide an appreciation for the range of techniques that have been developed, rather than to simply list sources. Segmentation methods are listed under four main headings. What may be termed the "classical" approach consists of methods that partition the input image into subimages, which are then classified. The operation of attempting to decompose the image into classifiable units is called "dissection." The second class of methods avoids dissection, and segments the image either explicitly, by classification of prespecified windows, or implicitly by classification of subsets of spatial features collected from the image as a whole. The third strategy is a hybrid of the first two, employing dissection together with recombination rules to define potential segments, but using classification to select from the range of admissible segmentation possibilities offered by these subimages. Finally, holistic approaches that avoid segmentation by recognizing entire character strings as units are described.
880 citations
01 May 2001
TL;DR: The historical evolution of CR systems is presented, the available CR techniques, with their superiorities and weaknesses, are reviewed and directions for future research are suggested.
Abstract: Character recognition (CR) has been extensively studied in the last half century and has progressed to a level that is sufficient to produce technology-driven applications. Now, rapidly growing computational power is enabling the implementation of the present CR methodologies and is creating an increasing demand in many emerging application domains which require more advanced methodologies. This paper serves as a guide and update for readers working in the CR area. First, the historical evolution of CR systems is presented. Then, the available CR techniques, with their superiorities and weaknesses, are reviewed. Finally, the current status of CR is discussed and directions for future research are suggested. Special attention is given to off-line handwriting recognition, since this area requires more research in order to reach the ultimate goal of machine simulation of human reading.
517 citations
TL;DR: There still is a great gap between human reading and machine reading capabilities, and a great amount of further effort is required to narrow-down this gap, if not bridge it.
508 citations
Additional excerpts
...Several research works have been focusing on new techniques and methods that would reduce the processing time while providing higher recognition accuracy [3]....
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