Skeletonization Algorithm for Binary Images

Numeral recognition remains one of the most important problems in pattern recognition. To the best of our knowledge, little work has been done in Devnagari script compared with those for non Indian scripts like Latin, Chinese and Japanese. In this paper we propose an effective method for recognition of isolated Marathi handwritten numerals written in Devnagari script. Fourier Descriptors that describe the shape of Marathi handwritten numerals are used as feature. 64 dimensional Fourier Descriptors represents the shape of numerals, invariant to rotation, scale and translation. Three different classifiers, namely, nearest neighborhood (NN), K-nearest neighborhood (KNN) and Support Vector Machine (SVM) are used independently in order to recognize test numeral. These classifiers are trained with 64 dimensional Fourier Descriptors (FD) of training samples. The proposed system is experimented with a database of 13000 samples of Marathi handwritten numerals using fivefold cross validation method for result computation. An overall recognition rate of 97.05%, 97.04%and 97.85% are obtained for NN, KNN and SVM respectively.

/pdf/fourier-descriptor-based-isolated-marathi-handwritten-23iqenl2pe.pdf

Fourier Descriptor based Isolated Marathi Handwritten Numeral Recognition

Offline Script Identification from multilingual Indic-script documents: A state-of-the-art

Script identification is being widely accepted techniques for selection of the particular script OCR (Optical Character Recognition) in multilingual document images. Extensive research has been done in this field, but still it suffers from low identification accuracy. This is due to the presence of faded document images, illuminations and positions while scanning. Noise is also a major obstacle in the script identification process. However, it can only be minimized up to a level, but cannot be removed completely. In this paper, an attempt is made to analyze and classify various script identification schemes for document images. The comparison is also made between these schemes, and discussion is made based upon their merits and demerits on a common platform. This will help the researchers to understand the complexity of the issue and identify possible directions for research in this field.

Script identification algorithms: a survey

Text recognition from natural scene images is very tough task now these days compare than videos. Application of image processing called pattern recognition make easy to recognize text from multimedia documents. A pattern can be fingerprint image, handwritten word sample, human face images, speech signal and DNA sequence etc or we can say that all pattern are in machine editable form. Text can be recognized with and without segmentation of character. Segmentation can be line, word or character level and without segmentation character is recognized from whole text image. Character recognition is a field of research and various research has been done in the area of pattern recognition. There we use a new technique called diagonal based feature extraction in last layer of convolutional neural network and make feature extraction easy with the help of genetic algorithm. After extraction of feature we provide training to extreme learning machine. Along this feature extraction technique we use feed forward network as a classifier and convolution neural network for feature extractor. It is a deep learning based technique of neural network which use for classification or recognition of text. This is basically used for providing training and in testing phase. CRConvNet has more layers working of all layer shown in flowchart. One dataset which contain 360 training set data that are all in capital(A-Z) and small(a-z) alphabet, digit (0–9) and some special character are also used. Another dataset contains samples of video and images (ICDAR 2003) for testing. Extensive studies shows that the recognition system which using diagonal based feature learning provide high recognition accuracy while requiring less time for training.

A deep learning based character recognition system from multimedia document

Automatic identification of a script in a given document image facilitates many important applications such as automatic archiving of multilingual documents, searching online archives of document images and for the selection of script specific OCR in a multilingual environment. In this paper a Zone-based Structural feature extraction algorithm scheme towards the recognition of South-Indian scripts along with English and Hindi is proposed. The document images are segmented into lines and the line image is divided into different zones and the structural features are extracted. A total of 37 features were extracted in the first level and then reduced to an optimal number of features using wrapper and filter selection approaches. The K-nearest neighbor and the support vector machine classifiers are used for classification and recognition purpose. A classification accuracy of 100% is achieved on the optimal feature set.

Zone-based structural feature extraction for script identification from Indian documents

Extensive research has been done on optical character recognition in the last few decades. Most of the efforts were made to develop OCR systems for foreign languages like English, Japanese, Roman and Arabic characters. Many commercial OCR systems for these foreign languages are available in the market. In the context of Indian languages, majority of work is reported on Hindi and Bangla. And very few reports are available on South Indian languages. This paper describes a character recognition system that can handle machine printed text documents in Kannada, which is the official language of the South Indian state of Karnataka. Initially, the scanned image is preprocessed to remove noise. Lines, words and character components are segmented using two-stage segmentation technique. Classification of the character components is done in two stages. In the first stage, the character components are grouped into small subsets by a feature based tree classifier. In the second stage, characters in each group are recognized using a nearest neighbor classifier. We adopted this hybrid approach instead of using only a tree classifier because it is nearly impossible to find a set of stroke features that are simple to compute, robust and reliable to detect, and are sufficient to classify a large number of basic and complex shaped compound characters. The system is tested with the data set containing 8400 characters of different font and size. On average, the system recognizes characters with an accuracy of about 92.68%.

Hierarchical Recognition System for MachinePrinted Kannada Characters

Script Identification from a given document image is an important process for many computer applications such as automatic archiving of multilingual documents, searching online archives of document images and for the selection of script specific OCR in a multilingual environment. In this paper a Zone-based Structural feature extraction algorithm towards the recognition of South-Indian scripts
along with English and Hindi is proposed. The document images are segmented into lines and the line image is divided into different zones and the structural features are extracted. A total of 37 features were extracted in the first level and then reduced to an optimal number of
features using wrapper and filter selection approaches. The K-nearest neighbor and the support vector machine classifiers are used for classification and recognition purpose. Very good classification accuracy is achieved on the optimal feature set

https://eprints.manipal.edu/149/1/34833098-Script-Identification-from-Multilingual-Indian-Documents-using-Structural-Features.pdf

Script Identification from Multilingual Indian Documents using Structural Features

The recognition of handwritten numeral is an important area of research for its applications in post office, banks and other organizations. This paper presents automatic recognition of handwritten Kannada numerals using both unsupervised and supervised classifiers. Four different types of structural features, namely, direction frequency code, water reservoir, end points and average boundary length from the minimal bounding box are used in the recognition of numeral. The effect of each feature and their combination in the numeral classification is analyzed. Combining classifiers has proved to be an
effective solution to several classification problems in pattern recognition. In the image classification, it is often beneficial to consider each feature type separately, and to integrate the initial classification results by a final classifier. In this paper, we developed a robust hybrid approach where fuzzy k-Nearest Neighbor (fuzzy k-NN) and fuzzy c-means (FCM) as base classifiers for individual feature sets, the results of which together forms the feature vector for the final k-Nearest Neighbor (k-NN)
classifier. Testing is done, using different feature sets, individually and in combination, on a database containing 1600 samples of different numerals and the results are compared with the different existing methods.

Combined Classifiers In Recognition Of Handwritten Kannada Numerals: A Hybrid Approach

As the number of networked computers grows, intrusion
detection becomes an essential component in keeping networks secure. Various approaches for intrusion detection are currently being in use with each one has its own merits and demerits. This paper presents the work to test and improve the performance of an intrusion detection system based on C-fuzzy decision tree, a new class of decision tree. The tree grows gradually by using fuzzy C-means clustering (FCM) algorithm to split the patterns in a selected node with the maximum heterogeneity into C corresponding children nodes. We investigated the usefulness of C-fuzzy decision tree for developing IDS with a data partition based on horizontal fragmentation. Emphirical results indicate the usefulness of our approach in developing the efficient IDS. This paper also used a modified fuzzy C-means algorithm with controllable membership ratio through an extended distance measure to include an additional higher order term. Effect of different membership ratio on the developed decision tree with each fragment is tested to select the best membership ratio. The results obtained have shown that our improved version performs better resulting in an effective intrusion detection system

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NV Subbareddy

Papers

Zone-based structural feature extraction for script identification from Indian documents

Hierarchical Recognition System for MachinePrinted Kannada Characters

Script Identification from Multilingual Indian Documents using Structural Features

Combined Classifiers In Recognition Of Handwritten Kannada Numerals: A Hybrid Approach

Intrusion Detection System using Modified C-Fuzzy DecisionTree Classifier