Gujarati handwritten numeral optical character reorganization through neural network

Identification of local regions from where optimal discriminating features can be extracted is one of the major tasks in the area of pattern recognition. To locate such regions different kind of region sampling techniques are used in the literature. There is no standard methodology to identify exactly such regions. Here we have proposed a methodology where local regions of varying heights and widths are created dynamically. Genetic algorithm (GA) is then applied on these local regions to sample the optimal set of local regions from where an optimal feature set can be extracted that has the best discriminating features. We have evaluated the proposed methodology on a data set of handwritten Bangla digits. In the present work, we have randomly generated seven sets of local regions and from every set, GA selects an optimal group of local regions which produces best recognition performance with a support vector machine (SVM) based classifier. Other popular optimization techniques like simulated annealing (SA) and hill climbing (HC) have also been evaluated with the same data set and maximum recognition accuracies were found to be 97%, 96.7% and 96.7% for GA, SA and HC, respectively. We have also compared the performance of the present technique with those of other zone based techniques on the same database.

A genetic algorithm based region sampling for selection of local features in handwritten digit recognition application

Survey Paper: A survey on multi-channel communication in wireless sensor networks

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.

Diagonal Based Feature Extraction for Handwritten Alphabets Recognition System using Neural Network

Offline handwriting recognition in Indian regional scripts is an interesting area of research as almost 460 million people in India use regional scripts. The nine major Indian regional scripts are Bangla (for Bengali and Assamese languages), Gujarati, Kannada, Malayalam, Oriya, Gurumukhi (for Punjabi language), Tamil, Telugu, and Nastaliq (for Urdu language). A state-of-the-art survey about the techniques available in the area of offline handwriting recognition (OHR) in Indian regional scripts will be of a great aid to the researchers in the subcontinent and hence a sincere attempt is made in this article to discuss the advancements reported in this regard during the last few decades. The survey is organized into different sections. A brief introduction is given initially about automatic recognition of handwriting and official regional scripts in India. The nine regional scripts are then categorized into four subgroups based on their similarity and evolution information. The first group contains Bangla, Oriya, Gujarati and Gurumukhi scripts. The second group contains Kannada and Telugu scripts and the third group contains Tamil and Malayalam scripts. The fourth group contains only Nastaliq script (Perso-Arabic script for Urdu), which is not an Indo-Aryan script. Various feature extraction and classification techniques associated with the offline handwriting recognition of the regional scripts are discussed in this survey. As it is important to identify the script before the recognition step, a section is dedicated to handwritten script identification techniques. A benchmarking database is very important for any pattern recognition related research. The details of the datasets available in different Indian regional scripts are also mentioned in the article. A separate section is dedicated to the observations made, future scope, and existing difficulties related to handwriting recognition in Indian regional scripts. We hope that this survey will serve as a compendium not only for researchers in India, but also for policymakers and practitioners in India. It will also help to accomplish a target of bringing the researchers working on different Indian scripts together. Looking at the recent developments in OHR of Indian regional scripts, this article will provide a better platform for future research activities.

Handwriting Recognition in Indian Regional Scripts: A Survey of Offline Techniques

Character recognition is the important area in image processing and pattern recognition fields. Handwritten character recognition has received extensive attention in academic and production fields. The recognition system can be either on-line or off-line. Off-line handwriting recognition is the subfield of optical character recognition. India is a multi-lingual and multi-script country, where eighteen official scripts are accepted and have over hundred regional languages. In this paper we propose Zone centroid and Image centroid based Distance metric feature extraction system. The character centroid is computed and the image (character/numeral) is further divided in to n equal zones. Average distance from the character centroid to the each pixel present in the zone is computed. Similarly zone centroid is computed and average distance from the zone centroid to each pixel present in the zone is computed. We repeated this procedure for all the zones/grids/boxes present in the numeral image. There could be some zones that are empty, and then the value of that particular zone image value in the feature vector is zero. Finally 2*n such features are extracted. Nearest neighbor and Feed forward back propagation neural network classifiers are used for subsequent classification and recognition purpose. We obtained 99 %, 99%, 96% and 95 % recognition rate for Kannada, Telugu, Tamil and Malayalam numerals respectively.

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Efficient zone based feature extration algorithm for handwritten numeral recognition of four popular south indian scripts

Problem statement: Efficient utilization of energy has been a core area of research in wireless sensor networks. Sensor nodes deployed in a network are battery operated. As batteries cannot be recharged frequently in the field setting, energy optimization becomes paramount in prolonging the battery-life and, consequently, the network lifetime. The communication module utilizes a major part of the energy expenditure of a sensor node. Hence data compression methods to reduce the number of bits to be transmitted by the communication module will significantly reduce the energy requirement and increase the lifetime of the sensor node. The present objective of the study contracted with the designing of efficient data compression algorithm, specifically suited to wireless sensor network. Approach: In this investigation, the natural correlation in a typical wireless sensor network data was exploited and a modified Huffman algorithm suited to wireless sensor network was designed. Results: The performance of the modified adaptive Huffman algorithm was analyzed and compared with the static and adaptive Huffman algorithm. The results indicated better compression ratio. Conclusion: Hence the proposed algorithm outperformed both static and adaptive Huffman algorithms, in terms of compression ratio and was well suited to embedding in sensor nodes for compressed data communication.

/pdf/design-of-modified-adaptive-huffman-data-compression-47g0slt7lu.pdf

Design of Modified Adaptive Huffman Data Compression Algorithm for Wireless Sensor Network

Handwritten character recognition has received extensive attention in academic and production fields. The recognition system can be either online or off-line. There is a large demand for Optical character recognition on hand written documents. India is a multi-lingual country and multi script country, where eighteen official scripts are accepted and have over hundred regional languages. In this paper we have proposed the projection distance metric and zoning based scheme for numeral recognition. We tested our proposed method for Kannada and Tamil numerals. A nearest neighbor classifier is used for subsequent purpose. The proposed method gives around 93% and 90% of recognition accuracy for Kannada and Tamil numerals respectively.

Isolated Handwritten Kannada and Tamil Numeral Recognition: A Novel Approach

Handwriting recognition has always been a challenging task in image processing and pattern recognition. There are five major stages in the handwritten character recognition problem: Image processing, segmentation, feature extraction, training and recognition, and preprocessing. India is a multi-lingual multiscript country, where eighteen official scripts are accepted and there are over a hundred regional languages. In this paper we propose a zone-based feature extraction algorithm scheme for the recognition of off-line handwritten numerals of four popular Indian scripts. The character centroid is computed and the character/numeral image (50x50) is further divided into 25 equal zones (10x10). The average distance from the character centroid to the pixels present in the zone column, is computed. This procedure is sequentially repeated for all the zone/grid/box columns present in the zone (10 features). Similarly, the average distance from the character centroid to the pixels present in the zone row is computed. This procedure is sequentially repeated for all the zone rows present in the zone (10 features). There could be some zone column/row that is empty of foreground pixels; then the feature value of that zone column/row in the feature vector is zero. This procedure is sequentially repeated for the entire zone present in the numeral image. Finally, 500 such features are extracted for classification and recognition. The nearest neighbor, feed forward back propagation neural network and support vector machine classifiers are used for subsequent classification and recognition purposes. We obtained a recognition rate of 98.65 % for Kannada numerals, 96.1 % for Tamil numerals, 98.6 % for Telugu numerals and 96.5 % for Malayalam numerals using the support vector machine.

http://www.jatit.org/volumes/research-papers/Vol7No1/8Vol7No1.pdf

Handwritten numeral/mixed numerals recognition of south-indian scripts: the zone- based feature extraction method

Character recognition is the important area in image processing and pattern recognition fields. Handwritten character recognition has received extensive attention in academic and production fields. The recognition system can be either on-line or off-line. Off-line handwriting recognition is the subfield of optical character recognition. India is a multi-lingual and multi-script country, where eighteen official scripts are accepted and have over hundred regional languages. In this paper we present Zone and Distance metric based feature extraction system. The character centroid is computed and the image is further divided in to n equal zones. Average distance from the character centroid to the each pixel present in the zone is computed. This procedure is repeated for all the zones present in the numeral image. Finally n such features are extracted for classification and recognition. Support vector machine is used for subsequent classification and recognition purpose. We obtained 97.75% recognition rate for Kannada numerals.

P. Vanaja Ranjan

Papers

Efficient zone based feature extration algorithm for handwritten numeral recognition of four popular south indian scripts

Design of Modified Adaptive Huffman Data Compression Algorithm for Wireless Sensor Network

Isolated Handwritten Kannada and Tamil Numeral Recognition: A Novel Approach

Handwritten numeral/mixed numerals recognition of south-indian scripts: the zone- based feature extraction method

Support Vector Machine based Handwritten Numeral Recognition of Kannada Script