The paper presents a two stage classification approach for handwritten Devanagari characters. The first stage is using structural properties like shirorekha, spine in character and second stage exploits some intersection features of characters which are fed to a feedforward neural network. Simple histogram based method does not work for finding shirorekha, vertical bar (spine) in handwritten Devnagari characters. So we designed a differential distance based technique to find a near straight line for shirorekha and spine. This approach has been tested for 50000 samples and we got 89.12% success.
GeometryNet can be considered as a lexical database for geometric entities and concepts. The idea is borrowed from WordNet, a popular knowledge repository often used for natural language processing tasks in AI applications. The basic objective behind the construction of a GeometryNet is to analyze and understand the geometric problems and draw relevant geometric figures automatically. Initial emphasis is put on machine understanding of problem statements that involve geometric constructions. School level geometry problems practiced by students of age group 13-16 are targeted. This paper explains different aspects of a GeometryNet, issues behind its construction, and its possible use for machine understanding of geometric problem statements.
This paper presents a comparative study of two different methods, which are based on fusion and polar transformation of visual and thermal images. Here, investigation is done to handle the challenges of face recognition, which include pose variations, changes in facial expression, partial occlusions, variations in illumination, rotation through different angles, change in scale etc. To overcome these obstacles we have implemented and thoroughly examined two different fusion techniques through rigorous experimentation. In the first method log-polar transformation is applied to the fused images obtained after fusion of visual and thermal images whereas in second method fusion is applied on log-polar transformed individual visual and thermal images. After this step, which is thus obtained in one form or another, Principal Component Analysis (PCA) is applied to reduce dimension of the fused images. Log-polar transformed images are capable of handling complicacies introduced by scaling and rotation. The main objective of employing fusion is to produce a fused image that provides more detailed and reliable information, which is capable to overcome the drawbacks present in the individual visual and thermal face images. Finally, those reduced fused images are classified using a multilayer perceptron neural network. The database used for the experiments conducted here is Object Tracking and Classification Beyond Visible Spectrum (OTCBVS) database benchmark thermal and visual face images. The second method has shown better performance, which is 95.71% (maximum) and on an average 93.81% as correct recognition rate.
Script identification for handwritten document image is an open document analysis problem especially for multilingual optical character recognition (OCR) system. To design the OCR system for multi-script document pages, it is essential to recognise different scripts before running a particular OCR system of a script. The present work reports an intelligent feature-based technique for word-level script identification in multi-script handwritten document pages. At first, the text lines and then the words are extracted from the document pages. A set of 39 distinctive features have been designed of which eight features are topological and the rest (31) are based on convex hull for each word image. For selection of a suitable classifier, performances of multiple classifiers are evaluated with the designed feature set on multiple subsets of freely available database CMATERdb1.5.1 (http://www.code.google.com/p/cmaterdb), which comprises of 150 handwritten document pages containing both Devnagari and Roman script words. Statistical significance tests on these performance measures declare MLP to be the best performing one. The overall word-level script identification accuracy with MLP classifier on the said database is observed as 99.74%.
Holistic word recognition attempts to recognize the entire word image as a single pattern. In general, it performs better than segmentation based word recognition model for known, fixed and small sized lexicon. The present work deals with recognition of handwritten words in Hindi in holistic way. Features like area, aspect ratio, density, pixel ratio, longest run, centroid and projection length are extracted either from entire word image or from the hypothetically generated sub-images of the same. An 89-elements feature vector has been designed to represent each word in the feature space and five different classifiers have been used for measuring recognition performances. Considering the complexities of Hindi characters, the technique shows an impressive result using a Multilayer Perceptron (MLP) based classifier. Moreover, the technique shows scale and rotation invariant nature to a significant extent.
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