Feature Design for Offline Arabic Handwriting Recognition: Handcrafted vs Automated?
Bibliographic record
Abstract
In handwriting recognition, design of relevant feature is a very important but daunting task. On one hand, handcraft design of features is difficult, depending on expert knowledge and on heuristics. On the other hand, biologically inspired neural networks are able to learn automatically features from the input image, but requires a good underlying model. The goal of this paper is to evaluate the performance of automatically learned features compared to handcrafted features, as they provide a promising alternative to the difficult task of features handcrafting. In this work, the recognition model is based on the long short-term memory (LSTM) and connectionist temporal classification (CTC) neural networks. This model has been shown to outperform the well-known HMM model for various handwriting tasks, thanks to its reliable probabilistic modeling. In its multidimensional form, called MDLSTM, this network is able to automatically learn features from the input image. For evaluation, we compare the MDLSTM learned features and four state-of-the-art handcrafted features. The IFN/ENIT database has been used as benchmark for Arabic word recognition, where the results are promising.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| 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 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".