Font and Size Independent OCR for Printed Kannada Documents using SVM Classifiers

The following topics were dealt with: document analysis and recognition; multimedia document processing; character recognition; document image processing; cheque processing; form processing; music processing; document segmentation; electronic documents; character classification; handwritten character recognition; information retrieval; postal automation; font recognition; Indian language OCR; handwriting recognition; performance evaluation; graphics recognition; oriental character recognition; and word recognition

Re-examination of one-point methods of liquid limit determination

This Paper deals with the analysis of liquid limit of soils, an inferential parameter of universal acceptance. It has been undertaken primarily to re-examine one-point methods of determination of liquid limit water contents. It has been shown by basic characteristics of soils and associated physico-chemical factors that critical shear strengths at liquid limit water contents arise out of force field equilibrium and are independent of soil type. This leads to the formation of a scientific base for liquid limit determination by one-point methods, which hitherto was formulated purely on statistical analysis of data. Available methods (Norman, 1959; Karlsson, 1961; Clayton & Jukes, 1978) of one-point liquid limit determination have been critically re-examined. A simple one-point cone penetrometer method of computing liquid limit has been suggested and compared with other methods. Experimental data of Sherwood & Ryley (1970) have been employed for comparison of different cone penetration methods. Results indicate that, apart from mere statistical considerations, one-point methods have a strong scientific base on the uniqueness of modified flow line irrespective of soil type. Normalized flow line is obtained by normalization of water contents by liquid limit values thereby nullifying the effects of surface areas and associated physico-chemical factors that are otherwise reflected in different responses at macrolevel.Cet article traite de l'analyse de la limite de liquidité des sols, paramètre déductif universellement accepté. Cette analyse a été entreprise en premier lieu pour ré-examiner les méthodes à un point destinées à la détermination de la teneur en eau à la limite de liquidité. Il a été démontré par les caractéristiques fondamentales de sols et par des facteurs physico-chimiques associés que les résistances critiques à la rupture au cisaillement pour des teneurs en eau à la limite de liquidité résultent de l'équilibre des champs de forces et sont indépendantes du type de sol concerné. On peut donc constituer une base scientifique pour la détermination de la limite de liquidité par des méthodes à un point lesquelles, jusqu'alors, n'avaient été formulées que sur la base d'une analyse statistique des données. Les méthodes dont on dispose (Norman, 1959; Karlsson, 1961; Clayton & Jukes, 1978) pour la détermination de la limite de liquidité à un point font l'objet d'un ré-examen critique. Une simple méthode d'analyse à un point à l'aide d'un pénétromètre à cône pour le calcul de la limite de liquidité a été suggérée et comparée à d'autres méthodes. Les données expérimentales de Sherwood & Ryley (1970) ont été utilisées en vue de comparer différentes méthodes de pénétration par cône. En plus de considérations d'ordre purement statistque, les résultats montrent que les méthodes de détermination à un point constituent une base scientifique solide en raison du caractère unique de la ligne de courant modifiée, quel que soit le type de sol La ligne de courant normalisée est obtenue par la normalisation de la teneur en eau en faisant appel à des valeurs de limite de liquidité pour, de cette manière, annuler les effets des surfaces et des facteurs physico-chimiques associés qui sans cela se manifesteraient dans les différentes réponses au niveau macro.

A blind indic script recognizer for multi-script documents

We report a hierarchical blind script identifier for 11 different Indian scripts. An initial grouping of the 11 scripts is accomplished at the first level of this hierarchy. At the subsequent level, we recognize the script in each group. The various nodes of this tree use different feature-classifier combinations. A database of 20,000 words of different font styles and sizes is collected and used for each script. Effectiveness of Gabor and Discrete Cosine Transform features has been independently, evaluated using nearest neighbor linear discriminant and support vector machine classifiers. The minimum and maximum accuracies obtained, using this hierarchical mechanism, are 92.2% and 97.6%, respectively.

Improved recognition of aged Kannada documents by effective segmentation of merged characters

In optical character recognition of very old books, the recognition accuracy drops mainly due to the merging or breaking of characters. In this paper, we propose the first algorithm to segment merged Kannada characters by using a hypothesis to select the positions to be cut. This method searches for the best possible positions to segment, by taking into account the support vector machine classifier's recognition score and the validity of the aspect ratio (width to height ratio) of the segments between every pair of cut positions. The hypothesis to select the cut position is based on the fact that a concave surface exists above and below the touching portion. These concave surfaces are noted down by tracing the valleys in the top contour of the image and similarly doing it for the image rotated upside-down. The cut positions are then derived as closely matching valleys of the original and the rotated images. Our proposed segmentation algorithm works well for different font styles, shapes and sizes better than the existing vertical projection profile based segmentation. The proposed algorithm has been tested on 1125 different word images, each containing multiple merged characters, from an old Kannada book and 89.6% correct segmentation is achieved and the character recognition accuracy of merged words is 91.2%. A few points of merge are still missed due to the absence of a matched valley due to the specific shapes of the particular characters meeting at the merges.