BanglaWild (English Prompt): leaderboard

BanglaWild (2026) tests Bengali scene text recognition on 1,014 in-the-wild mobile-phone photographs of walls, signboards, vehicles, banners and product packaging, scored against verbatim human transcriptions. Character error rate over Unicode code points after NFC normalization, times 100; lower is better, and it can exceed 100 when a model emits long off-target output. Vision-language models get an English transcribe-only instruction with the image alone; conventional OCR engines run without a prompt.

Metric: Character error rate (CER x100: character edit distance over Unicode code points after NFC normalization divided by the reference length, mean over the 1,014-image test split; not capped, so it can exceed 100 when a model emits long off-target output; zero-shot, temperature 0 or greedy, 150-token cap; English transcribe-only prompt P1, image only). Source: arxiv.org. Saturation forecast: Around May 2027. 18 models tracked.

Top models

#ModelScoreOverall rank
1Gemini 2.5 Flash14.08#452
2Qwen 3.5 9B20.38#803
3Qwen 3.5 35B A3B21.2#480
4Claude Sonnet 4.623.15#158
5Qwen 3.5 27B23.87#358
6Gemini 2.5 Pro24.85#262
7Llama 4 Maverick36.96#1105
8GPT-5.4 Mini53.41#389
9Gemma 3 12B (IT)63.89#1849
10Claude Haiku 4.590.47#568
11Gemma 3 4B (IT)134.82#3848
12GPT-5.4 Nano246.95#633

No result here: #1 Claude Fable 5.2, #2 Gemini 4 Carbon, #5 Claude Opus 5.5.

Interactive version: theaggregate.ai/benchmark?slug=banglawild-english-prompt · How It Works · Data refreshed daily, snapshot 2026-11-12.