In this paper we present a system towards the recognition of off-line Oriya handwritten characters. Since most of the Oriya characters have curve-like stroke, we use curvature feature for the recognition purpose. To g...
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In this paper we present a system towards the recognition of off-line Oriya handwritten characters. Since most of the Oriya characters have curve-like stroke, we use curvature feature for the recognition purpose. To get the feature, at first, the input image is size normalized and segmented into 49times49 blocks. Curvature is then computed using bi-quadratic interpolation method and quantized into 3 levels according to concave, linear and convex regions. Next direction of gradient is quantized into 32 levels with pi/16 intervals, and strength of the gradient is accumulated in each of the 32 directions and in each of the 3 curvature levels of every block. A spatial resolution is made to get 7times7 blocks from 49times49 blocks and a directional resolution is made to get 8 directions from 32 directions. Using curvature features for 3 levels we get 1176 (7times7 blocks times 8 directions times 3 levels) dimensional features. Finally using principal component analysis we reduce the dimension 1176 to 392 and this 392 dimensional feature vector is fed to a quadratic classifier for recognition. We tested 18190 samples of Oriya handwritten samples and obtained 94.60% accuracy from our proposed system.
Text/graphics separation in document image analysis is one of the main concerns in present research work. The complexity enhances when both text and graphics overlap in the context of maps in color images. This paper ...
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Text/graphics separation in document image analysis is one of the main concerns in present research work. The complexity enhances when both text and graphics overlap in the context of maps in color images. This paper discusses a number of improvements to text/graphics separation methods to make it suitable for maps. Emphasize is given to the overlapping regions of text and graphics. It also discusses a method of color separation using clustering method for the purpose of text/graphics separation
This paper presents a pioneering study on automatic dating of handwritten manuscripts. Analysis of handwriting style forms the core of the dating method. Initially, it is hypothesized that a manuscript can be dated, t...
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This paper presents a pioneering study on automatic dating of handwritten manuscripts. Analysis of handwriting style forms the core of the dating method. Initially, it is hypothesized that a manuscript can be dated, to a certain level of accuracy, by looking at the way it is written. The hypothesis is then verified with real samples of known dates. A general framework is proposed for machine dating of handwritten manuscripts. Experiments on a database containing manuscripts of Gustave Flaubert (1821- 1880), the famous French novelist reports about 62% accuracy when manuscripts are dated within a range of five calendar years with respect to their exact year of writing.
This paper deals with a quadratic classifier based scheme for the recognition of off-line handwritten numerals of Kannada, an important indian script. The features used in the classifier are obtained from the directio...
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This paper deals with a quadratic classifier based scheme for the recognition of off-line handwritten numerals of Kannada, an important indian script. The features used in the classifier are obtained from the directional chain code information of the contour points of the characters. The bounding box of a character is segmented into blocks and the chain code histogram is computed in each of the blocks. Here we have used 64 dimensional and 100 dimensional features for a comparative study on the recognition accuracy of our proposed system. This chain code features are fed to the quadratic classifier for recognition. We tested our scheme on 2300 data samples and obtained 97.87% and 98.45% recognition accuracy using 64 dimensional and 100 dimensional features respectively, from the proposed scheme using five-fold cross-validation technique.
Struck-out words are often found in handwritten manuscripts. A realistic off-line handwriting recognition system should take care of this common aspect. A simple but efficient approach to this problem is to subject ea...
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This paper is concerned with research on OCR (optical character recognition) of printed mathematical expressions. Construction of a representative corpus of technical and scientific documents containing expressions is...
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recognition of handwritten characters is difficult because of variability involved in the writing style of different individuals. This paper deals with recognition of off-line Bangla handwritten characters using quadr...
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No significant research work towards recognition of handwritten Bangla characters has yet been done. Only a few works in this area are found in the literature which are based on small databases col-lected in laborator...
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This paper deals with segmentation and recognition of touching characters appearing in scanned mathematical expressions. The technique is based on multifactorial analysis that integrates several factors determining cu...
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This paper deals with segmentation and recognition of touching characters appearing in scanned mathematical expressions. The technique is based on multifactorial analysis that integrates several factors determining cut-positions in a touching character image. A predictive algorithm is developed for efficient selection of possible cut-positions for segmenting touching characters. Experiment has been carried out using a test-set of reasonable size and results show that a considerable improvement in recognition accuracy can be achieved with a modest increase in computations.
Postal automation is a topic of research over the last few years. There are many works towards the postal automation in USA, UK, Japan and Australia, but for indian postal automation there is no significant work. This...
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Postal automation is a topic of research over the last few years. There are many works towards the postal automation in USA, UK, Japan and Australia, but for indian postal automation there is no significant work. This paper deals with word-wise handwritten script identification for indian postal automation. In the proposed scheme at first document skew is detected and corrected. Non-text parts are then segmented from the document using run length smoothing algorithm (RLSA). Next, using a piecewise projection method the destination address block (DAB), is at first segmented into lines and then into words. Using water reservoir concept we compute the busy-zone of the word. Finally, using matra/Shirorekha, water reservoir concept based feature, fractal based feature, etc. a neural network (NN) classifier is generated for word-wise Bangla and English scripts identification. Overall accuracy of the proposed system is at present 9 7.62%.
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