Research on Chemical Expression Images Recognition

Paper Information

Citation: Hong, C., Du, X., & Zhang, L. (2015). Research on Chemical Expression Images Recognition. Proceedings of the 2015 Joint International Mechanical, Electronic and Information Technology Conference, 267-271. https://doi.org/10.2991/jimet-15.2015.50

Publication: JIMET 2015 (Atlantis Press)

Additional Resources:

• JSME Editor (used for visualization)

What kind of paper is this?

Method. The paper proposes a novel algorithmic pipeline (OCSR) for recognizing 2D organic chemical structures from images. It validates this method by comparing it against an existing tool (OSRA) using a quantitative metric (Tanimoto Coefficient) on a test set of 200 images.

What is the motivation?

A vast amount of chemical structural information exists in scientific literature (PDFs/images) that is not machine-readable. Manually converting these images to formats like InChI or CML is labor-intensive. Existing tools face challenges with:

1. Adhesion: Poor separation when chemical symbols touch or overlap with bonds.
2. Stereochemistry: Incomplete identification of “real” (solid) and “virtual” (dashed/hashed) wedge bonds.

What is the novelty here?

The authors propose a specific OCSR (Optical Chemical Structure Recognition) workflow with two key technical improvements:

1. Vector-based Separation: Instead of purely pixel-based processing, the method vectorizes the image (using Potrace) to extract straight lines and curves, allowing better separation of “adhesive” chemical symbols (like H, N, O attached to bonds).
2. Stereochemical Logic: Specific rules for identifying wedge bonds:
   • Virtual (Dashed) Wedges: Identified by grouping connected domains and checking linear correlation of their center points.
   • Real (Solid) Wedges: Identified after thinning by analyzing linear correlation and width variance of line segments.

What experiments were performed?

• Dataset: 200 chemical structure images collected from the network.
• Baselines: Compared against OSRA (Optical Structure Recognition Application), a free online tool.
• Metric: Tanimoto Coefficient, measuring the similarity of the set of recognized bonds and symbols against the ground truth.

What outcomes/conclusions?

• Performance: The proposed OCSR method achieved higher recognition rates than OSRA.
  • Exact Match (100%): OCSR achieved 90.0% vs. OSRA’s 82.2%.
  • High Similarity (>85%): OCSR recognized 157 structures vs. OSRA’s 114.
• Limitations: The paper notes that “real wedge” and “virtual wedge” identification was a primary focus, but general recognition effectiveness still “has room for improvement”.

Reproducibility Details

Data

The study used a custom collection of images, not a standard benchmark.

Algorithms

The recognition pipeline follows these specific steps:

1. Preprocessing:
   • Grayscale: via cvCvtColor (OpenCV).
   • Binarization: via Otsu’s method.
2. Isolated Symbol Removal:
   • Identifies connected domains with aspect ratios in [0.8, 3.0].
   • Recognizes them using OCR (GOCR, OCRAD, Tesseract) and removes them from the image.
3. Virtual Wedge Recognition:
   • Groups small connected domains (points/clumps).
   • Calculates linear correlation of center points; if collinear, treats as a dashed bond.
4. Vectorization & Thinning:
   • Thinning: Rosenfeld algorithm (optimized) to reduce lines to single pixel width.
   • Vectorization: Uses Potrace to convert pixels to vector segments.
   • Merging: Combines split vector segments based on angle thresholds to form long straight lines.
5. Adhesive Symbol Separation:
   • Identifies curves (short segments after vectorization) attached to long lines.
   • Separates these domains and re-runs OCR.
6. “Super Atom” Merging:
   • Merges adjacent vertical/horizontal symbols (e.g., “HO”, “CH3”) based on distance thresholds between bounding boxes.

Models

The system relies on off-the-shelf OCR tools for character recognition; no custom ML models were trained.

• OCR Engines: GOCR, OCRAD, TESSERACT.
• Visualization: JSME (JavaScript Molecule Editor) used to render output strings.

Evaluation

Hardware

• Requirements: Not specified, but runs on standard CPU architecture (implied by use of standard libraries like OpenCV and Potrace).

Citation

@inproceedings{hongResearchChemicalExpression2015,
  title = {Research on {{Chemical Expression Images Recognition}}},
  booktitle = {Proceedings of the 2015 {{Joint International Mechanical}}, {{Electronic}} and {{Information Technology Conference}}},
  author = {Hong, Chen and Du, Xiaoping and Zhang, Lu},
  year = {2015},
  publisher = {Atlantis Press},
  address = {Chongqing, China},
  doi = {10.2991/jimet-15.2015.50},
  isbn = {978-94-6252-129-2}
}