Archival Handwritten Digits Identification Through Deep Learning Models Johnson
Bibliographic record
Abstract
This research is inspired by the work of climate scientists to analyse archival handwritten documents and predict reliably changes in the climate. The aim of this work is to better understand recognition of handwritten documents especially focusing on archival maritime logs. Indeed, OCR (Optical Character Recognition) has existed for many years. The shortcomings of this widely used method, however, are manifesting in frequent confusion of digits and letters when it comes to archival handwritten documents. The conversation of perfecting the methods of automated recognition of handwritten characters has been evolving in recent years. With the advent of deep learning methods, new tools are considered within the problem space. In this extension of thesis work, two such methods are put to the test -convolutional and long-short term memory (LSTM) neural networks (NN). The applicability of several state-of-the-art models is considered with detailed experiments and comparative analysis. A compound model of convolutional NN followed by LSTM is also considered. While all models register high accuracy, it is observed that the compound model performs faster with accuracy above the lone CNN.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".