One of the major reasons for the poor recognition rate is error in character segmentation. In this paper we propose an OCR for printed Hindi text in Devanagari script, using Artificial Neural Network (ANN), which improves its efficiency. As there is no separation between the characters of texts written in Hindi as there is in English, the Optical Character Recognition (OCR) systems developed for the Hindi language carry a very poor recognition rate. Hindi is the most widely spoken language in India, with more than 300 million speakers. In order to show the effectiveness of the proposed technique extensive experiments have been performed on smart phone and the results are compared with various top-ranked commercial mobile apps. The system tends to build up an application that can recognize the English content present in an image captured by using a smart phone and then display the recognized text into editable format on the mobile screen. The proposed system is an image processing and data retrieval system for English text. This study aim is to provide a flexible method to extract texts from challenging images and show them line-by-line by employing basic image processing techniques.

Text detection is an important step towards the various content-based image retrieval techniques (CBIR). Even though, a bunch of research has been carried out in this area but still there is a room for the improvement as none of the presented methods are error-free due to the challenges that may include the font of the characters, multi-oriented text, and the quality of pictures that contain text. The field of text recognition is an important area as it finds various useful applications in computer vision such as document analysis, image search, robot navigation etc.

Text extractor api camera