Cursive text detection is essential in various fields, includ- ing document analysis, scene character and optical charac- ter recognition (OCR). Despite technological advancements, accurate detection and recognition of cursive text in natu- ral scene images continued to be difficult because of differ- ences in font sizes, styles, orientations, alignments, resolu- tions, blurriness, complex backgrounds and appearance of multilingual text. Urdu is a cursive language widely spoken in many South-Asian countries. There has been a persistent need for Urdu text recognition because of its appearance in natural scenes such as signboards, car number plates, news- papers, magazines, etc. Moreover, Urdu text detection is challenging due to its complex writing style, which includes joined writing, variations in the same characters, numerous ligatures, multiple baselines, and other factors. This paper proposes two hybrid models for resolution-free cursive text detection and recognition. Firstly, a convolutional neural net- work (CNN) is used for text detection, which is repeated with the Visual Geometry Group (VGG-16). Secondly, for text recognition, Long Short-Term Memory (LSTM) model is used on the extracted features from CNN and VGG-16 separately. The proposed hybrid models CNN in combination with LSTM and VGG-16 in combination with LSTM outperform the ex- isting ones by achieving 91% and 96% accuracy, respectively.