菜单
Evaluates text fidelity and typography accuracy in AI-generated images by measuring spelling, case sensitivity, repetition, and structural inconsistencies against reference prompts. Use when the user has predictions and gold and needs to compute ABHINAW Score.
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
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| name | abhinaw-score |
| description | Evaluates text fidelity and typography accuracy in AI-generated images by measuring spelling, case sensitivity, repetition, and structural inconsistencies against reference prompts. Use when the user has predictions and gold and needs to compute ABHINAW Score. |
| metadata | {"skill_kind":"metric","source_arxiv":2409.11874,"bibtex_key":"jagtap2024abhinaw","confidence":"medium"} |
ABHINAW: A method for Automatic Evaluation of Typography within AI-Generated Images — Jagtap et al. (2024) (arXiv:2409.11874, 2024)
Evaluates text fidelity and typography accuracy in AI-generated images by measuring spelling, case sensitivity, repetition, and structural inconsistencies against reference prompts.
ABHINAW Score (primary) — range: [0, 1]
Input: AI-generated images paired with their corresponding reference text prompts.
Output: A normalized accuracy measure (ABHINAW Score) per image, averaged across 100 GPT-based OCR runs per image.
scores = []
for img in images:
img_runs = []
for _ in range(10):
gen_text = custom_gpt_ocr(img)
ref_vec = embed(lowercase(ref_text))
gen_vec = embed(lowercase(gen_text))
sim = cosine_similarity(ref_vec, gen_vec)
if sim > 0.9:
sim = allow_rearrangements(sim)
sim = apply_brevity_adjustment(sim, gen_text, ref_text)
img_runs.append(sim)
scores.append(mean(img_runs))
return mean(scores)
In the next step, we have developed a mathematical framework where finally we are calculating the ABHINAW Score here are the main highlights of the framework, the stored text were converted to lower case (reason for incorporating the lower case is already discussed in section[3.1]). Then the generated text were converted to vectors and we calculate the cosine similarity for all the generated text (see section[3.2]). If the cosine similarity is greater than 0.9 then only the text rearrangements are permitted in the scoring. In the final step, the text redundancy is taken into account by introduction of brevity adjustment (See section[4.3]). The scores were then averaged to provide a normalized accuracy measure.
@misc{jagtap2024abhinaw,
title={ABHINAW: A method for Automatic Evaluation of Typography within AI-Generated Images},
author={Jagtap et al. (2024)},
year={2024},
note={arXiv:2409.11874}
}