With the increase of the number of documents available in the digital environment, efficient access to the documents becomes crucial. Manual indexing of the documents is costly; however, and can be carried out only in limited amounts. Therefore, automatic analysis of documents is crucial. Although plenty of effort has been spent on optical character recognition (OCR), most of the existing OCR systems fail to address the challenge of recognizing characters in historical documents on account of the poor quality of old documents, the high level of noise factors, and the variety of scripts. More importantly, OCR systems are usually language dependent and not available for all languages. Word spotting techniques have been recently proposed to access the historical documents with the idea that humans read whole words at a time. In these studies the words rather than the characters are considered as the basic units. Due to the poor quality of historical documents, the representation and matching of words continue to be challenging problems for word spotting. In this study we address these challenges and propose a simple but effective method for the representation of word images by a set of line descriptors. Then, two different matching criteria making use of the line-based representation are proposed. We apply our methods on the word spotting and redif extraction tasks. The proposed line-based representation does not require any specific pre-processing steps, and is applicable to different languages and scripts. In word spotting task, our results provide higher scores than the existing word spotting studies in terms of retrieval and recognition performances. In the redif extraction task, we obtain promising results providing a motivation for further and advanced studies on Ottoman literary texts.