This paper presents a novel approach for text line extraction which is based on Gaussian scale space, a dedicated binarization, and an energy minimization framework. It enhances the text lines in the image using multi-scale anisotropic second derivative of Gaussian filter bank at the average height of the text line. It then applies a binarization, which is based on component-tree and is tailore...

The paper introduces a testing framework for the evaluation and validation of text line segmentation algorithms. Text line segmentation represents the key action for correct optical character recognition. Many of the tests for the evaluation of text line segmentation algorithms deal with text databases as reference templates. Because of the mismatch, the reliable testing framework is required. ...

This work presents a two-stage text line detection method for historical documents. In a first stage, a deep neural network called ARU-Net labels pixels to belong to one of the three classes: baseline, separator or other. The separator class marks beginning and end of each text line. The ARU-Net is trainable from scratch with manageably few manually annotated example images (less than 50). This...

This paper proposes an approach to water flow method modification for text segmentation and reference text line detection of sample text at almost any skew angle. Original water flow algorithm assumes hypothetical water flows under only a few specified angles of the document image frame from left to right and vice versa. As a result of water flow algorithm, unwetted image frames are extracted. ...

PhoneStation is a system that provides a Sun Microsystems SPARCstation† with complete control over an ordinary telephone line. It consists of a telephone line interface unit with loop control and touch tone detection, a suite of supporting software libraries that include digital signal processing for call progress monitoring, text-to-speech conversion, telephone line control, 2. PhoneStation Sy...

Text in images and video frames carries important information for visual content understanding and retrieval. In this paper, by using multiscale wavelet features, we propose a novel coarse-to-fine algorithm that is able to locate text lines even under complex background. First, in the coarse detection, after the wavelet energy feature is calculated to locate all possible text pixels, a density-...

text classification is an important research field in information retrieval and text mining. the main task in text classification is to assign text documents in predefined categories based on documents’ contents and labeled-training samples. since word detection is a difficult and time consuming task in persian language, bayesian text classifier is an appropriate approach to deal with different...

We present a demonstration of a newly developed text stream event detection method on over a million articles from the New York Times corpus. The event detection is designed to operate in a predominantly on-line fashion, reporting new events within a specified timeframe. The event detection is achieved by detecting significant changes in the statistical properties of the text where those proper...

The following work describes our solutions to the detection and tracking problems defined by the Topic Detection and Tracking (TDT2) research initiative. We discuss the implementation and results of the approaches which were recently tested on the TDT2 evaluation corpus. Our solutions to these problems extend text-based ranked retrieval techniques previously used for document clustering and fil...