Handwritten Chemical Symbol Recognition Using SVMs

Paper Information

Citation: Tang, P., Hui, S. C., & Fu, C. W. (2013). Online Chemical Symbol Recognition for Handwritten Chemical Expression Recognition. 2013 IEEE/ACIS 12th International Conference on Computer and Information Science (ICIS), 535-540. https://doi.org/10.1109/ICIS.2013.6607894

Publication: IEEE ICIS 2013

What kind of paper is this?

This is a Method paper according to the AI for Physical Sciences taxonomy.

• Dominant Basis: The authors propose a novel hybrid architecture (SVM-EM) that combines two existing techniques to solve a specific recognition problem.
• Rhetorical Indicators: The paper explicitly defines algorithms (Algorithm 1 & 2), presents a system architecture, and validates the method via ablation studies comparing the hybrid approach against its individual components.

What is the motivation?

Entering chemical expressions on digital devices is difficult due to their complex 2D spatial structure.

• The Problem: While handwriting recognition for text and math is mature, chemical structures involve unique symbols and spatial arrangements that existing tools struggle to process efficiently.
• Existing Solutions: Standard tools (like ChemDraw) rely on “point-click-and-drag” interactions, which are described as complicated and non-intuitive compared to direct handwriting.
• Goal: To enable fluid handwriting input on pen/touch-based devices (like iPads) by accurately recognizing individual chemical symbols in real-time.

What is the novelty here?

The core contribution is the Hybrid SVM-EM approach, which splits recognition into a coarse classification stage and a fine-grained verification stage.

• Two-Stage Pipeline:
  1. SVM Recognition: Uses statistical features (stroke count, turning angles) to generate a short-list of candidate symbols.
  2. Elastic Matching (EM): Uses a geometric point-to-point distance metric to re-rank these candidates against a library of stored symbol prototypes.
• Online Stroke Partitioning: A heuristic-based method to group strokes into symbols in real-time based on time adjacency (grouping the last $n$ strokes) and spatial intersection checks, without waiting for the user to finish the entire drawing.

What experiments were performed?

The authors conducted a user study to collect data and evaluate the system:

• Participants: 10 users were recruited to write chemical symbols on an iPad.
• Task: Each user wrote 78 distinct chemical symbols (digits, alphabets, bonds) 3 times each.
• Baselines: The hybrid method was compared against two baselines:
  1. SVM only
  2. Elastic Matching only.
• Metrics: Evaluation focused on Precision@k (where $k=1, 3, 5$), measuring how often the correct symbol appeared in the top-$k$ suggestions.

What were the outcomes and conclusions drawn?

The hybrid approach demonstrated superior performance compared to using either technique in isolation.

• Key Results:
  • Hybrid SVM-EM: 89.7% Precision@1 (Top-1 accuracy).
  • SVM Only: 85.1% Precision@1.
  • EM Only: 76.7% Precision@1.
• Category Performance: The system performed best on Operators (91.9%) and Digits (91.3%), with slightly lower performance on Alphabetic characters (88.6%).
• Impact: The system was successfully implemented as a real-time iOS application, allowing users to draw complex structures like $C#CC(O)$ which are then converted to SMILES strings.

Reproducibility Details

Data

The study generated a custom dataset for training and evaluation.

Algorithms

The pipeline consists of four distinct algorithmic steps:

1. Stroke Partitioning

• Logic: Groups the most recently written stroke with up to the last 4 previous strokes.
• Filtering: Invalid groups are removed using “Spatial Distance Checking” (strokes too far apart) and “Stroke Intersection Checking” (strokes that don’t intersect where expected).

2. Preprocessing

• Size Normalization: Scales symbol to a standard size based on its bounding box.
• Smoothing: Uses average smoothing (replacing mid-points with the average of neighbors) to remove jitter.
• Sampling: Resamples valid strokes to a fixed number of 50 points.

3. SVM Feature Extraction

• Horizontal Angle: Calculated between two consecutive points ($P_1, P_2$). Values are binned into 12 groups ($30^{\circ}$ each).
• Turning Angle: The difference between two consecutive horizontal angles. Values are binned into 18 groups ($10^{\circ}$ each).
• Features: Input vector consists of stroke count, normalized coordinates, and the percentage of angles falling into the histograms described above.

4. Elastic Matching (Verification)

• Distance Function: Euclidean distance summation between the points of the candidate symbol ($s$) and the partitioned input ($s_p$). $$D(s, s_p) = \sum_{j=1}^{n} \sqrt{(x_{s,j} - x_{p,j})^2 + (y_{s,j} - y_{p,j})^2}$$ Note: The paper formula sums the distances; $n$ is the number of points (50).
• Ranking: Candidates are re-ranked in ascending order of this elastic distance.

Models

• Classifier: Linear Support Vector Machine (SVM) implemented using LibSVM.
• Symbol Library: A “Chemical Elastic Symbol Library” stores the raw stroke point sequences for all 78 supported symbols to enable the elastic matching comparison.

Evaluation

Performance was measured using precision at different ranks (Top-N accuracy).

Hardware

• Device: Apple iPad (iOS platform).
• Input: Touch/Pen-based input recording digital ink (x, y coordinates and pen-up/down events).

Citation

@inproceedings{tangOnlineChemicalSymbol2013,
  title = {Online Chemical Symbol Recognition for Handwritten Chemical Expression Recognition},
  booktitle = {2013 IEEE/ACIS 12th International Conference on Computer and Information Science (ICIS)},
  author = {Tang, Peng and Hui, Siu Cheung and Fu, Chi-Wing},
  year = 2013,
  pages = {535--540},
  publisher = {IEEE},
  doi = {10.1109/ICIS.2013.6607894}
}