A stochastic computing architecture for local contrast and mean image thresholding algorithm
Bibliographic record
Abstract
Abstract Image binarization algorithms in document image analysis divide pixel values into two groups, including white as background and black as foreground. Among others, the local contrast and mean (LCM)‐based thresholding algorithm offers excellent performance in processing degraded documents. This algorithm, however, is susceptible to noise and requires significant hardware resources. In this paper, an energy‐efficient and fault‐tolerant architecture is proposed for implementing the LCM algorithm in stochastic computing (SC). Leveraging correlated input bitstreams, this architecture saves energy and improves the fault tolerance of the implementation. Experimental results show that the proposed LCM stochastic architecture significantly outperforms the stochastic implementation of the Sauvola algorithm in terms of both binarization accuracy and hardware overhead and energy consumption. Even using 16‐bit streams, the proposed circuit produces an error rate lower than 5%. The stochastic implementation of the LCM algorithm using a 16‐bit length FSM‐based LD sequence is 22 times less in area, 26 times less in total power, 28 times less in energy consumption and more fault‐tolerant than the conventional 8‐bit bit‐width weighted binary with the same frequency constraints.
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 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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".