Automated Latin Text Detection in Document Images and Natural Scene Images based on Connected Component Analysis

Robust and accurate detection of text in natural scene images and document images is a very challenging and common research problem. Over the past few decades, a variety of algorithms for text detection in images have been developed but there is still need for more robust and accurate text detection methods. In this work, we have proposed an accurate and robust text detection framework in which canny edge detection, maximally stable extremal regions and geometric filtering are employed in combination to efficiently collect and filter letter candidates in an image. Subsequently, individual letter patches are grouped to detect text sequences, which are then fragmented into isolated word patches. Finally, optical character recognition is employed to digitize the word patches. The proposed algorithm is tested on images representing different scenarios ranging from documents to natural scenes. Promising results have been reported which prove the accuracy and robustness of the proposed framework and encourage its practical implementation in real world applications.