On the Recognition of Printed Characters of Any Font and Size
IEEE Transactions on Pattern Analysis and Machine Intelligence
Invariant Image Recognition by Zernike Moments
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Survey of Methods and Strategies in Character Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Skew Angle Detection of Digitized Indian Script Documents
IEEE Transactions on Pattern Analysis and Machine Intelligence
A model-based segmentation method for handwritten numeral strings
Computer Vision and Image Understanding - Special issue on document image understanding and retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
A rotation invariant printed Chinese character recognition system
Pattern Recognition Letters
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Rotation-invariant pattern matching using wavelet decomposition
Pattern Recognition Letters
Word and Sentence Extraction Using Irregular Pyramid
DAS '02 Proceedings of the 5th International Workshop on Document Analysis Systems V
Alignment of Free Layout Color Texts for Character Recognition
ICDAR '01 Proceedings of the Sixth International Conference on Document Analysis and Recognition
Recognition of Rotated Characters by Eigen-space
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 2
Recognition of Indian Multi-oriented and Curved Text
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
Generalizations of angular radial transform for 2D and 3D shape retrieval
Pattern Recognition Letters
OCR Fonts Revisited for Camera-Based Character Recognition
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 02
Recognition of English Multi-oriented Characters
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 02
Extraction of Embedded Class Information from Universal Character Pattern
ICDAR '07 Proceedings of the Ninth International Conference on Document Analysis and Recognition - Volume 01
Engineering Applications of Artificial Intelligence
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Multioriented and curved text lines extraction from Indian documents
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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There are printed complex documents where text lines of a single page may have different orientations or the text lines may be curved in shape. As a result, it is difficult to detect the skew of such documents and hence character segmentation and recognition of such documents are a complex task. In this paper, using background and foreground information we propose a novel scheme towards the recognition of Indian complex documents of Bangla and Devnagari script. In Bangla and Devnagari documents usually characters in a word touch and they form cavity regions. To take care of these cavity regions, background information of such documents is used. Convex hull and water reservoir principle have been applied for this purpose. Here, at first, the characters are segmented from the documents using the background information of the text. Next, individual characters are recognized using rotation invariant features obtained from the foreground part of the characters. For character segmentation, at first, writing mode of a touching component (word) is detected using water reservoir principle based features. Next, depending on writing mode and the reservoir base-region of the touching component, a set of candidate envelope points is then selected from the contour points of the component. Based on these candidate points, the touching component is finally segmented into individual characters. For recognition of multi-sized/multi-oriented characters the features are computed from different angular information obtained from the external and internal contour pixels of the characters. These angular information are computed in such a way that they do not depend on the size and rotation of the characters. Circular and convex hull rings have been used to divide a character into smaller zones to get zone-wise features for higher recognition results. We combine circular and convex hull features to improve the results and these features are fed to support vector machines (SVM) for recognition. From our experiment we obtained recognition results of 99.18% (98.86%) accuracy when tested on 7515 (7874) Devnagari (Bangla) characters.