The on-going growth of e-business has resulted in companies having to manage an ever increasing number of product, packaging and promotional images. Systems for indexing and retrieving such images are required in order to ensure image libraries can be managed and fully exploited as valuable business resources. In this paper, we explore the power of text recognition for e-business image management and propose an innovative system based on photo OCR. Photo OCR has been actively studied for scene text recognition but has not been exploited for e-business digital image management. Besides the well known difficulties in scene text recognition such as various size, location, orientation in text and cluttered background, e-business images typically feature text with extremely diverse fonts, and the characters are often artistically modified in shape, colour and arrangement. To address these challenges, our system takes advantage of the combinatorial power of deep neural networks and MSER processing. The cosine distance and n-gram vectors are used during retrieval for matching detected text to queries to provide tolerance to the inevitable transcription errors in text recognition. To evaluate our proposed system, we prepared a novel dataset designed specifically to reflect the challenges associated with text in e-business images. We compared our system with two other approaches for scene text recognition, and the results show our system outperforms other state-ofthe-art on the new challenging dataset. Our system demonstrates that recognizing text embedded in images can be hugely beneficial for digital asset management.