2026-07-08 04:00 UTCOriginal source2 min readUpdated: 2026-07-08 08:22 UTC

BaFCo: A Document Understanding Benchmark for Complex Bangla Form Comprehension

BaFCo is a benchmark dataset for Bangla form comprehension, comprising 200 multi-page complex Bangladeshi government forms from diverse sectors. It features 26 fine-grained and 5 coarse entity types. Evaluations of latest MLLMs show limitations in localizing granular form entities.

SourcearXiv Computational LinguisticsAuthor: Abu Tyeb Azad, Ishita Sur Apan, Fahim Ahmed, Sumaiya Karim Katha, Ezharuddin Jubaer, Armun Alam, Pranjal Kumar Nandi, Amin Ahsan Ali, Aman Chadha, Md Mofijul Islam, AKM Mahbubur Rahman

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[Submitted on 6 Jul 2026]

Title:BaFCo: A Document Understanding Benchmark for Complex Bangla Form Comprehension

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Abstract:Document comprehension is a challenging yet impactful task for Multimodal Large Language Models, especially as these systems see growing adoption in real-world, human-centric applications. However, this adoption is limited for low-resource languages such as Bangla due to the scarcity of high-quality annotated data. To address this gap, we introduce BaFCo, a benchmark dataset for Bangla form comprehension with a focus on Document Layout Analysis (DLA) and Key Information Extraction (KIE). BaFCo curates 200 multi-page complex Bangladeshi government forms, sourced from across diverse sectors including agriculture, education, banking, and land management. To accurately capture the structural and contextual complexity of these forms, we define a fine-grained annotation schema comprising 26 types of form entities, along with a separate coarse form entity set consisting of 5 types. We evaluate the latest MLLMs from the ChatGPT, Gemini, Claude, Qwen, and Kimi series using zero-shot and chain-of-thought prompts under both low and high reasoning setups. Our results reveal limitations in current MLLMs' ability in comprehending Bangla forms, particularly in accurately localizing highly granular form entities. Our dataset and code is available at: this https URL

Comments: Accepted at the 19th European Conference on Computer Vision (ECCV), 2026

Subjects:

Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)

Cite as: arXiv:2607.05614 [cs.CL]

(or arXiv:2607.05614v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2607.05614

arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Fahim Ahmed [view email] [v1] Mon, 6 Jul 2026 20:17:38 UTC (23,180 KB)

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