Handwritten Gurmukhi Digit Recognition System for Small Datasets
Bibliographic record
Abstract
In the present study, a method to increase the recognition accuracy of Gurmukhi (Indian Regional Script) Handwritten Digits has been proposed. The proposed methodology uses a DCNN (Deep Convolutional Neural Network) with a cascaded XGBoost (Extreme Gradient Boosting) algorithm. Also, a comprehensive analysis has been done to apprehend the impact of kernel size of DCNN on recognition accuracy. The reason for using DCNN is its impressive performance in terms of recognition accuracy of handwritten digits, but in order to achieve good recognition accuracy, DCNN requires a huge amount of data and also significant training/testing time. In order to increase the accuracy of DCNN for a small dataset more images have been generated by applying a shear transformation (A transformation that preserves parallelism but not length and angles) to the original images. To address the issue of large training time only two hidden layers along with selective cascading XGBoost among the misclassified digits have been used. Also, the issue of overfitting is discussed in detail and has been reduced to a great extent. Finally, the results are compared with performance of some recent techniques like SVM (Support Vector Machine) Random Forest, and XGBoost classifiers on DCT (Discrete Cosine Transform) and DWT (Discrete Wavelet Transform) features obtained on the same dataset. It is found that proposed methodology can outperform other techniques in terms of overall rate of recognition.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.006 | 0.004 |
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".