Several approaches were proposed in order to extract text from scanned documents. However, text extraction in heterogeneous documents stills a real challenge. Indeed, text extraction in this context is a difficult task because of the variation of the text due to the differences of sizes, styles and orientations, as well as to the complexity of the document region background. Recently, we have proposed the improved hybrid binarization based on Kmeans method (I-HBK)5 to extract suitably the text from heterogeneous documents. In this method, the Page Layout Analysis (PLA), part of the Tesseract OCR engine, is used to identify text and image regions. Afterwards our hybrid binarization is applied separately on each kind of regions. In one side, gamma correction is employed before to process image regions. In the other side, binarization is performed directly on text regions. Then, a foreground and background color study is performed to correct inverted region colors. Finally, characters are located from the binarized regions based on the PLA algorithm. In this work, we extend the integration of the PLA algorithm within the I-HBK method. In addition, to speed up the separation of text and image step, we employ an efficient GPU acceleration. Through the performed experiments, we demonstrate the high F-measure accuracy of the PLA algorithm reaching 95% on the LRDE dataset. In addition, we illustrate the sequential and the parallel compared PLA versions. The obtained results give a speedup of 3.7x when comparing the parallel PLA implementation on GPU GTX 660 to the CPU version.