ARTICLE TEXT EXTRACTOR MANUAL

The system automatically performs the text block and text line detection to be fed into the interactive computer assisted transcription. In this paper we present an end-to-end system that takes as input a page image and provides a fully correct transcript with the help of user interaction. These techniques work at line-level thus the transcription task assumes that the page images have been correctly decomposed into the relevant text line images. An alternative that can speed up and lower the cost of this process is the use of computer assisted, interactive techniques.

ARTICLE TEXT EXTRACTOR MANUAL

Unfortunately manual transcription is generally too expensive and, in most cases, current automatic techniques fail to provide the required level of accuracy. To provide access to the contents of the document collections that are being digitized, transcription is required. The evaluation shows that (1) DivaDiaWI has two advantages when bringing it into context with state-of-the-art tools, (2) the automatic functions of DivaDiaWI greatly accelerate the GT generation, and (3) DivaDiaWI obtains a high score in a system usability test. For other region types such as background and decorations, users can manually draw their GT with user-friendly operations. DivaDiaWI applies automatic functions using Gabor features to generate GT of text lines. In this article, we propose to use Gabor features to generate GT, and based on Gabor features, we developed a web-based interface called DivaDiaWI. Although there exist many methods and tools in GT generation, most of them are not suitable on our specific data sets. text line and decoration) annotated by human experts. To train a layout analysis system, an essential prerequisite is a set of pages with corresponding ground truth (GT), i.e. It aims at splitting a document image into regions of interest such as text lines, background, and decorations. In the pipeline of automatic document image analysis (DIA), layout analysis is an important prerequisite for further steps including optical character recognition, script analysis, and image recognition. Historical documents usually have a complex layout, making them one of the most challenging types of documents for automatic image analysis. Finally, we present the datasets which are widely used for word spotting, we describe the evaluation standards and measures applied for performance assessment and discuss the results achieved by the state of the art. After presenting the core steps which compose a word spotting system, we investigate the use of retrieval enhancement techniques based on relevance feedback which improve the retrieved results. The nature of texts and inherent challenges addressed by word spotting methods are thoroughly examined.

This work aims to review the recent approaches as well as fill the gaps in several topics with respect to the related works. However, there exist very few comprehensive studies which analyze the various aspects of a word spotting system. Over the past decade there has been a growing interest in addressing document indexing using word spotting which is reflected by the continuously increasing number of approaches. In this framework, word spotting is an alternative solution to optical character recognition (OCR), which is rather inefficient for recognizing text of degraded quality and unknown fonts usually appearing in printed text, or writing style variations in handwritten documents. Vast collections of documents available in image format need to be indexed for information retrieval purposes.

Article text extractor