Showing papers by "Partha Pratim Roy published in 2009"

Multi-Oriented and Multi-Sized Touching Character Segmentation Using Dynamic Programming

Abstract: In this paper, we present a scheme towards the segmentation of English multi-oriented touching strings into individual characters. When two or more characters touch, they generate a big cavity region at the background portion. Using Convex Hull information, we use these background information to find some initial points to segment a touching string into possible primitive segments (a primitive segment consists of a single character or a part of a character). Next these primitive segments are merged to get optimum segmentation and dynamic programming is applied using total likelihood of characters as the objective function. SVM classifier is used to find the likelihood of a character. To consider multi-oriented touching strings the features used in the SVM are invariant to character orientation. Circular ring and convex hull ring based approach has been used along with angular information of the contour pixels of the character to make the feature rotation invariant. From the experiment, we obtained encouraging results.

Seal Detection and Recognition: An Approach for Document Indexing

Abstract: Reliable indexing of documents having seal instances can be achieved by recognizing seal information. This paper presents a novel approach for detecting and classifying such multi-oriented seals in these documents. First, Hough Transform based methods are applied to extract the seal regions in documents. Next, isolated text characters within these regions are detected. Rotation and size invariant features and a Support Vector Machine based classifier have been used to recognize these detected text characters. Next, for each pair of character, we encode their relative spatial organization using their distance and angular position with respect to the centre of the seal, and enter this code into a hash table. Given an input seal, we recognize the individual text characters and compute the code for pair-wise character based on the relative spatial organization. The code obtained from the input seal helps to retrieve model hypothesis from the hash table. The seal model to which we get maximum hypothesis is selected for the recognition of the input seal. The methodology is tested to index seal in rotation and size invariant environment and we obtained encouraging results.

Touching text character localization in graphical documents using SIFT

Abstract: Interpretation of graphical document images is a challenging task as it requires proper understanding of text/graphics symbols present in such documents. Difficulties arise in graphical document recognition when text and symbol overlapped/touched. Intersection of text and symbols with graphical lines and curves occur frequently in graphical documents and hence separation of such symbols is very difficult. Several pattern recognition and classification techniques exist to recognize isolated text/symbol. But, the touching/overlapping text and symbol recognition has not yet been dealt successfully. An interesting technique, Scale Invariant Feature Transform (SIFT), originally devised for object recognition can take care of overlapping problems. Even if SIFT features have emerged as a very powerful object descriptors, their employment in graphical documents context has not been investigated much. In this paper we present the adaptation of the SIFT approach in the context of text character localization (spotting) in graphical documents. We evaluate the applicability of this technique in such documents and discuss the scope of improvement by combining some state-of-the-art approaches.

A complete system for detection and recognition of text in graphical documents using background information

Abstract: Automatic Text/symbols retrieval in graphical documents (map, engineering drawing) involves many challenges because they are not usually parallel to each other. They are multi-oriented and curve in nature to annotate the graphical curve lines and hence follow a curvi-linear way too. Sometimes, text and symbols frequently touch/overlap with graphical components (river, street, border line) which enhances the problem. For OCR of such documents we need to extract individual text lines and their corresponding words/characters. In this paper, we propose a methodology to extract individual text lines and an approach for recognition of the extracted text characters from such complex graphical documents. The methodology is based on the foreground and background information of the text components. To take care of background information, water reservoir concept and convex hull have been used. For recognition of multi-font, multi-scale and multi-oriented characters, Support Vector Machine (SVM) based classifier is applied. Circular ring and convex hull have been used along with angular information of the contour pixels of the characters to make the feature rotation and scale