The paper describes a general framework for mining large amounts of text data from a defined set of Web pages. The acquired data are meant to constitute a corpus for training robust and reliable language models and thus the framework needs to also incorporate algorithms for appropriate text processing and duplicity detection in order to secure quality and consistency of the data. As we expect the resulting corpus to be very large, we have also implemented topic detection algorithms that allow us to automatically select subcorpora for domain-specific language models. The description of the framework architecture and the implemented algorithms is complemented with a detailed evaluation section. It analyses the basic properties of the gathered Czech corpus containing more than one billion text tokens collected using the described framework, shows the results of the topic detection methods and finally also describes the design and outcomes of the automatic speech recognition experiments with domain-specific language models estimated from the collected data.