Chemical Structure Recognition (CSR) System: Automatic Analysis of 2D Chemical Structures in Document Images In this era of advanced technology and automation, information extraction has become a very common practice for the analysis of data. A technique known as Optical Character Recognition (OCR) is used for recognition of text. The purpose is to extract textual data for automatic information analysis or natural language processing of document images. However, in the field of cheminformatics where it is required to recognize 2D molecular structures as they are published in research journals or patent documents, OCR is not adequate for processing, as chemical compounds can be represented both in textual as well as in graphical format. The digital representation of an image based chemical structure allows not only patent analysis teams to provide customize insights but also cheminformatic research groups to enhance their molecular structure databases, which further can be used for querying structure as well as substructural patterns. Some tools have been made for extraction and processing of imagebased molecular structures. Optical Structure Recognition Application (OSRA) being one of the tools that partially fulfill the task of recognizing chemical structural in document images into chemical formats (SMILES, SDF, or MOL). However, it has few problems such as poor character recognition, false structure extraction, and slow processing. In this paper, we have developed a prototype Chemical Structure Recognition (CSR) system using modern and advanced image processing opensource libraries, which allows us to extract structural information of a chemical structure embedded in the form of a digital raster image. The CSR system is capable of processing chemical information contained in chemical structure image and generates the SMILES or MOL representation. For performance evaluation, we have used two different data sets to measure the potential of the CSR system. It yields better results than OSRA that depict accurate recognition, fast extraction, and correctness of great significance.

Image processing technology for text recognition Image recognition and optical character recognition technologies have become an integral part of our everyday life due in part to the everincreasing power of computing and the ubiquity of scanning devices. Printed documents can be quickly converted into digital text files through optical character recognition and then be edited by the user. Consequently, minimal time is required to digitize documents; this is particularly helpful when archiving volumes of printed materials. This study demonstrates how imageprocessing technologies can be used in combination with optical character recognition to improve recognition accuracy and to improve the efficiency of extracting text from images. Two software systems are developed and tested during this study: a character recognition system applied to cosmeticrelated advertising images and a text detection and recognition system for natural scenes. The results of the experiment demonstrate that the proposed systems can accurately recognize text in images.