Srirangaraj Setlur

A Steerable Directional Local Profile Technique for Extraction of Handwritten Arabic Text Lines

In this paper, we present a new text line extraction method for handwritten Arabic documents. The proposed technique is based on a generalized adaptive local connectivity map (ALCM) using a steerable directional filter. The algorithm is designed to solve the particularly complex problems seen in handwritten documents such as fluctuating, touching or crossing text lines. The proposed algorithm consists of three steps. Firstly, a steerable filter is used to probe and determine foreground intensity along multiple directions at each pixel while generating the ALCM. The ALCM is then binarized using an adaptive thresholding algorithm to get a rough estimate of the location of the text lines. In the second step, connected component analysis is used to classify text and non text patterns in the generated ALCM to refine the location of the text lines. Finally, the text lines are separated by superimposing the text line patterns in the ALCM on the original document image and extracting the connected components covered by the pattern mask. Analysis of experimental results on the DARPA MADCAT Arabic handwritten document data indicate that the method is robust and is capable of correctly isolating handwritten text lines even on challenging document images.

Text extraction from gray scale historical document images using adaptive local connectivity map

This paper presents an algorithm using adaptive local connectivity map for retrieving text lines from the complex handwritten documents such as handwritten historical manuscripts. The algorithm is designed for solving the particularly complex problems seen in handwritten documents. These problems include fluctuating text lines, touching or crossing text lines and low quality image that do not lend themselves easily to binarizations. The algorithm is based on connectivity features similar to local projection profiles, which can be directly extracted from gray scale images. The proposed technique is robust and has been tested on a set of complex historical handwritten documents such as Newtons and Galileos manuscripts. A preliminary testing shows a successful location rate of above 95% for the test set.

Guide to OCR for Indic Scripts

Research on OCR of Indian scripts is gaining momentum in recent times. Many projects funded by government and industry are currently underway to scan hundreds of thousands of indic-script documents and manuscripts to create large digital library archives to preserve these treasures for posterity. OCR is a key enabling technology for making these archives practically accessible to researchers and lay users alike by creating search-able indexes and machine readable text repositories of these documents. This book provides an overview of the current state-of-the–art in the OCR of the different Indic scripts as well as other issues in the creation of accessible digital libraries for Indic scripts. It provides a good technical overview of the latest research in the field.

Text - image separation in Devanagari documents

In this paper we present a top-down, projection-profilebased algorithm to separate text blocks from image blocksin a Devanagari document. We use a distinctive feature ofDevanagari text, called Shirorekha (Header Line) to analyzethe pattern produced by Devanagari text in the horizontalprofile. The horizontal profile corresponding to a textblock possesses certain regularity in frequency, orientationand shows spatial cohesion. The algorithm uses these featuresto identify text blocks in a document image containingboth text and graphics.

Markov Random Field Based Text Identification from Annotated Machine Printed Documents

In this paper, we describe an approach to segment handwritten text, machine printed text and noise from annotated machine printed documents. Three categories of word level features are extracted. We use a modified K-Means clustering algorithm for classification followed by a relabeling procedure using Markov Random Field(MRF) based on a concept of neighboring patches and Belief Propagation(BP)rules. Experimental results on an imbalanced data set show that our approach achieves an overall recall of 96.33% .

Creation of data resources and design of an evaluation test bed for Devanagari script recognition

The Indian subcontinent has a large number of languages, dialects, and scripts with the Devanagari script being the primary and most widely used of all the scripts. To date, much of the Devanagari optical character recognition (OCR) research has been restricted to a handful of groups. So, techniques have not yet been widely disseminated or evaluated independently and automated evaluation tools are currently not available for lack of a standard representation of ground-truth and result data. A key reason for the absence of sustained research efforts in off-line Devanagari OCR appears to be the paucity of data resources. Ground truthed data for words and characters, on-line dictionaries, corpora of text documents and reliable, standardized statistical analyses and evaluation tools are currently lacking. So, the creation of such data resources will undoubtedly provide a much needed fillip to researchers working on Devanagari OCR. This paper describes a National Science Foundation sponsored project under the International Digital Libraries program to create data resources that will facilitate development of Devanagari OCR technology and provide a standardized test bed and evaluation tools for Devanagari script recognition.

Handwritten text separation from annotated machine printed documents using Markov Random Fields

The convenience of search, both on the personal computer hard disk as well as on the web, is still limited mainly to machine printed text documents and images because of the poor accuracy of handwriting recognizers. The focus of research in this paper is the segmentation of handwritten text and machine printed text from annotated documents sometimes referred to as the task of “ink separation” to advance the state-of-art in realizing search of hand-annotated documents. We propose a method which contains two main steps—patch level separation and pixel level separation. In the patch level separation step, the entire document is modeled as a Markov Random Field (MRF). Three different classes (machine printed text, handwritten text and overlapped text) are initially identified using G-means based classification followed by a MRF based relabeling procedure. A MRF based classification approach is then used to separate overlapped text into machine printed text and handwritten text using pixel level features forming the second step of the method. Experimental results on a set of machine-printed documents which have been annotated by multiple writers in an office/collaborative environment show that our method is robust and provides good text separation performance.

A Framework for Hand Gesture Recognition and Spotting Using Sub-gesture Modeling

Hand gesture interpretation is an open research problem in Human Computer Interaction (HCI), which involves locating gesture boundaries (Gesture Spotting) in a continuous video sequence and recognizing the gesture. Existing techniques model each gesture as a temporal sequence of visual features extracted from individual frames which is not efficient due to the large variability of frames at different timestamps. In this paper, we propose a new sub-gesture modeling approach which represents each gesture as a sequence of fixed sub-gestures (a group of consecutive frames with locally coherent context) and provides a robust modeling of the visual features. We further extend this approach to the task of gesture spotting where the gesture boundaries are identified using a filler model and gesture completion model. Experimental results show that the proposed method outperforms state-of-the-art Hidden Conditional Random Fields (HCRF) based methods and baseline gesture spotting techniques.

Challenges in OCR of Devanagari documents

OCR of Devanagari script presents a wide range of challenges that are not seen in Latin based scripts. This paper outlines the implementation of a neural network based Devanagari OCR. Experimental results on a standard data set are reported and analyzed.

Devanagari OCR using a recognition driven segmentation framework and stochastic language models

This paper describes a novel recognition driven segmentation methodology for Devanagari Optical Character Recognition. Prior approaches have used sequential rules to segment characters followed by template matching for classification. Our method uses a graph representation to segment characters. This method allows us to segment horizontally or vertically overlapping characters as well as those connected along non-linear boundaries into finer primitive components. The components are then processed by a classifier and the classifier score is used to determine if the components need to be further segmented. Multiple hypotheses are obtained for each composite character by considering all possible combinations of the classifier results for the primitive components. Word recognition is performed by designing a stochastic finite state automaton (SFSA) that takes into account both classifier scores as well as character frequencies. A novel feature of our approach is that we use sub-character primitive components in the classification stage in order to reduce the number of classes whereas we use an n-gram language model based on the linguistic character units for word recognition.