An Approach to a Comprehensive Test Framework for Analysis and Evaluation of Text Line Segmentation Algorithms

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. Hence, a new approach to a comprehensive experimental framework for the evaluation of text line segmentation algorithms is proposed. It consists of synthetic multi-like text samples and real handwritten text as well. Although the tests are mutually independent, the results are cross-linked. The proposed method can be used for different types of scripts and languages. Furthermore, two different procedures for the evaluation of algorithm efficiency based on the obtained error type classification are proposed. The first is based on the segmentation line error description, while the second one incorporates well-known signal detection theory. Each of them has different capabilities and convenience, but they can be used as supplements to make the evaluation process efficient. Overall the proposed procedure based on the segmentation line error description has some advantages, characterized by five measures that describe measurement procedures

Estimation of the Text Skew in the Old Printed Documents

Old printed documents represent the significant part of our heritage. In order to preserve them, the digitalization is indispensable. The paper proposed a robust skew estimation method for old printed document. It is based on the connected components made by filled convex hulls around text element. The connected components are enlarged by oriented morphological operation. Then, the longest connected component is extracted. The global orientation of the document is detected by its orientation. Accordingly, document image was globally de-skewed. The algorithm is tested on synthetic and real datasets. Obtained results proved the algorithmscorrectness

Range Detection of the Extremely Low-Frequency Magnetic Field Produced by Laptop’s AC Adapter

Human exposure to extremely low frequency magnetic field represents a risk to their health. This paper takes into consideration the level of an extremely low-frequency magnetic field between 30 and 300 Hz emitted by an AC laptop adapter. The experiment consists of testing 17 different AC adapters for laptops. During the testing, laptops are operated in a normal operating conditions as well as under heavy load. The magnetic field measurement is conducted in the area around the AC adapter. Obtained data is evaluated according to the critical level of the magnetic field proposed by safety standards. Furthermore, data is classified by a K-medians method in order to determine the critical levels of the magnetic field exposure in the nearby area of the AC adapter. Obtained classifications are evaluated according to safety standards, giving a critical analysis of magnetic field areas at risk. Due to emission of a very strong magnetic field in certain areas, a recommendation for safety use of the AC adapter is proposed