Improving Word Spotting System Performance using Ensemble Classifier Combination Methods
Bibliographic record
Abstract
The effective retrieval of information from scanned handwritten documents is becoming essential with the increasing amounts of digitized documents. Therefore, developing efficient means of analyzing and recognizing these documents is of significant interest. Among these methods is word spotting, which has recently become an active research area. Different ensemble classifiers have been successfully proposed to improve the performance of a pattern recognition or a word spotting system. In this paper, we propose an enhanced internal structure of the Arabic handwritten word spotting hierarchical classifier. In addition, we propose two ensemble classifier combination methods to improve the performance of closed lexicon word spotting systems. These methods are, 1) the improved score word matching method, and 2) score evaluation method. Both methods calculate a new score by utilizing the confidence values (scores) given by the combined classifiers. Support Vector Machines (SVM) and Regularized Discriminant Analysis (RDA) have been utilized to implement the proposed ensemble classifier. The proposed methods have been tested using the CENPARMI Arabic handwritten documents database, and the results show that combining classifiers has a significant improvement on word spotting systems. The precision rate increased by 4% and 17% respectively, when the improved score matching method and the score evaluation method have been used.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".