Chemical Structure Recognition Tool for Hand-Drawn Images
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Solution Overview
Problem
Existing chemical structure recognition tools face difficulties in processing live images from webcams to extract chemical data from hand-drawn images, making it challenging to convert these images into formats suitable for simulation and modeling in cheminformatics without manual intervention.
Innovation Solution
A Chemical Structure Recognition Tool (CSRT) that uses an image scanner and analyzer to process hand-drawn or digital images by converting them into grayscale, binarizing, smoothing, recognizing circle bonds, predicting OCR regions, thinning images, detecting edges, and generating output files in formats like .mol or .sdf, thereby extracting chemical data without manual input.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing chemical structure recognition tools are used to process hand-drawn images from webcams, then chemical data extraction is attempted, but recognition accuracy remains low (around 30%) and manual intervention is still required
Solution Approach 1:
The system performs preliminary image processing actions including converting images to grayscale, binarizing, smoothing, and detecting edges before chemical structure recognition. These preliminary actions prepare the image data in advance to improve subsequent recognition accuracy and enable fully automated processing
Solution Approach 2:
The patent introduces an intermediary processing pipeline between image capture and chemical structure recognition. This intermediary layer includes image preprocessing modules that transform raw webcam images into processed images suitable for accurate chemical structure analysis, bridging the gap between low-quality input and high-accuracy output
2Measurement precision
If chemical structure recognition tools process images through multiple processing steps (grayscale conversion, binarization, smoothing, edge detection), then image quality and recognition accuracy improve, but processing time and computational complexity increase
Solution Approach 1:
The image processing pipeline is segmented into distinct functional modules: grayscale conversion, binarization, smoothing, edge detection, and chemical structure recognition. Each module performs a specific transformation independently, allowing for optimized processing of each step and potential parallel execution to reduce overall processing time
3Adaptability or versatility
If existing tools extract chemical data from printed documents, then data extraction is achieved, but they fail to handle hand-drawn images from webcams effectively
Solution Approach 1:
The system adapts to different image types by dynamically adjusting processing parameters. For hand-drawn images from webcams, the system applies specific parameter settings for grayscale conversion, binarization thresholds, smoothing strength, and edge detection sensitivity that are optimized for the characteristics of hand-drawn sketches rather than printed documents
Solution Approach 2:
The patent creates a universal image processing framework that can handle multiple image types including hand-drawn images from webcams, scanned documents, and digital images. The same core processing pipeline (grayscale→binarization→smoothing→edge detection→recognition) serves all input types, with adjustable parameters to accommodate different characteristics
Data Source
AI summary
A method of extracting and then reusing/remodeling chemical data from a hand written or digital input image without manual inputs using Chemical Structure Recognition Tool (CSRT) is disclosed herein. It comprises loading said input image, converting said input image into a grayscale image i.e. stretching of loaded input image, converting said grayscale image into a binary image i.e. binarization, smoothing to reduce noise within said binary image, recognizing circle bond to identify presence of a circle inside a ring, predicting OCR region to find zones containing text, image thinning to identify specific shapes within said binary image, edge detection to detect image contrast, detecting double and triple bond, and obtaining output files.