Papers with recognition accuracy
MinerU2.5: A Decoupled Vision-Language Model for Efficient High-Resolution Document Parsing (2026.acl-industry)
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Junbo Niu, Zheng Liu, Zhuangcheng Gu, Bin Wang, Linke Ouyang, Zhiyuan Zhao, Tao Chu, Tianyao He, Fan Wu, Qintong Zhang, Zhenjiang Jin, Guang Liang, Rui Zhang, Wenzheng Zhang, Yuan Qu, Zhifei Ren, Yuefeng Sun, Zirui Tang, Boyu Niu, Yuanhong Zheng, Dongsheng Ma, Ziyang Miao, Hejun Dong, Siyi Qian, Junyuan Zhang, Fangdong Wang, Jingzhou Chen, Xiaomeng Zhao, Liqun Wei, Wei Li, Shasha Wang, RuiLiang Xu, Yuanyuan Cao, Lu Chen, Qianqian Wu, Huaiyu Gu, Lindong Lu, Dechen Lin, null Shenguanlin, Xuanhe Zhou, Linfeng Zhang, Yuhang Zang, Xiaoyi Dong, Jiaqi Wang, Bo Zhang, Lei Bai, Pei Chu, Weijia Li, Jiang Wu, Lijun Wu, Zhenxiang Li, Guangyu Wang, Zhongying Tu, Chao Xu, Kai Chen, Bowen Zhou, Dahua Lin, Wentao Zhang, Conghui He
| Challenge: | Document images are characterized by higher resolutions, denser content, and more complex structural layouts. |
| Approach: | They propose a 1.2B-parameter document parsing vision-language model that decouples layout analysis from local content recognition. |
| Outcome: | The proposed model surpasses general-purpose and domain-specific models on multiple benchmarks while maintaining significantly lower computational overhead. |
Emotion Impacts Speech Recognition Performance (N19-3)
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| Challenge: | Existing studies show that speech recognition systems depend on multiple factors including lexical content, speaker identity and dialect. |
| Approach: | They propose a method that evaluates the impact of emotion on recognition even when manual transcripts are not available. |
| Outcome: | The proposed method allows to evaluate the impact of emotion on recognition even when manual transcripts are not available. |
Lacuna Reconstruction: Self-Supervised Pre-Training for Low-Resource Historical Document Transcription (2022.findings-naacl)
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| Challenge: | Document transcription models are limited by extremely varied style and content across domains. |
| Approach: | They propose a self-supervised approach for learning rich visual representations for both handwritten and printed historical document transcription using a heterogeneous set of handwritten Islamicate manuscript images and early modern English printed documents. |
| Outcome: | The proposed model improves on a supervised model with as few as 30 line image transcriptions on two languages with a single line of image training. |
GraDeT-HTR: A Resource-Efficient Bengali Handwritten Text Recognition System utilizing Grapheme-based Tokenizer and Decoder-only Transformer (2025.emnlp-demos)
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| Challenge: | Bengali is the sixth most spoken language in the world, but handwritten text recognition systems for the language are underdeveloped. |
| Approach: | They propose a Bengali handwritten text recognition system that uses a decoder-only transformer to address the unique challenges of Bengali script. |
| Outcome: | The proposed system significantly improves on existing tokenizers on Bengali script. |
Thesis Proposal: Comparing Human and Model Perception of Writing Style under Controlled Perturbations (2026.eacl-srw)
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| Challenge: | This thesis examines how humans and models perceive writing style under controlled perturbations. |
| Approach: | They examine how humans and models perceive writing style under controlled perturbations . they also examine whether perturbations that reduce algorithmic recognition obscure stylistic identity . |
| Outcome: | The proposed research compares models and humans to find out how linguistic cues affect writing style . it will clarify how linguistic cue contributes differently to human and algorithmic perception of style - a cnn.com article argues . |
LLM ContextBridge: A Hybrid Approach for Intent and Dialogue Understanding in IVSR (2025.coling-industry)
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| Challenge: | In-vehicle speech recognition systems struggle with interpreting user intent accurately due to limitations in contextual understanding and ambiguity resolution. |
| Approach: | They propose a hybrid architecture that integrates Pretrained Language Model-based intent classification with Large Language Models to enhance both command recognition and dialogue management. |
| Outcome: | The proposed architecture improves recognition accuracy and user experience in multi-turn dialogues. |
AesX: Enhance Your Images with Stunning Aesthetic Beauty (2026.acl-industry)
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| Challenge: | Existing models do not analyze human preferences at a finer granularity, which leads to quality issues. |
| Approach: | They propose a set of preference indicators across two major dimensions, text-image consistency and aesthetic quality, and a generative framework to steer the model toward a generation path that more closely aligns with human aesthetic sensibilities. |
| Outcome: | The proposed model improves target recognition accuracy and overall visual aesthetic presentation by focusing on human preferences. |
ICLER: Intent CLassification with Enhanced Reasoning (2025.findings-emnlp)
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| Challenge: | Existing methods for intent classification are inadequate in identifying micro-grained intentions . ICLER is based on In-Context Learning, but it is inadequate in enterprise vertical domains . |
| Approach: | They propose an intent classification method with enhanced reasoning that optimizes the embedding model to capture subtle sentence-level information. |
| Outcome: | The proposed method outperforms existing methods in intent identification tasks in vertical domains. |
Are You for Real? Detecting Identity Fraud via Dialogue Interactions (D19-1)
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| Challenge: | Existing methods to detect identity fraud are prone to errors and are not based on real data. |
| Approach: | They propose to use a KG constructor and structured dialogue management to detect identity fraud in loan applications to generate questions based on personal information. |
| Outcome: | The proposed system can detect fraudsters and achieve higher recognition accuracy compared with rule-based systems. |
SNuC: The Sheffield Numbers Spoken Language Corpus (2022.lrec-1)
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| Challenge: | SNuC is the first published corpus of spoken alphanumeric identifiers . it contains recordings and transcriptions of over 50 native British English speakers . |
| Approach: | They present a corpus of spoken alphanumeric identifiers of the sort typically used as serial and part numbers in the manufacturing sector. |
| Outcome: | The proposed corpus can be used to improve spoken alphanumeric identifier recognition. |
Class-based LSTM Russian Language Model with Linguistic Information (2020.lrec-1)
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| Challenge: | LSTM models can be used in speech recognition systems at N-best or lattice rescoring stage. |
| Approach: | They propose to use word frequency and linguistic information to generate class-based LSTM Russian language models with various numbers of classes. |
| Outcome: | The proposed models outperform word-based models and word2vec models in terms of perplexity, training time, and word error rate. |
Correcting Pronoun Homophones with Subtle Semantics in Chinese Speech Recognition (2024.lrec-main)
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| Challenge: | Chinese speech recognition is becoming prevalent due to the similar semantic context of the entities and the overlap of Chinese pronunciation. |
| Approach: | They propose three models to address common confusion issues in Chinese speech recognition . they implement a language model, a LSTM model with semantic features and a rule-based assisted Ngram model . |
| Outcome: | The proposed models achieve highest recognition rate for “T” correction with improvements from 70% in the popular voice input methods up to 90%. |
Joint Audio/Text Training for Transformer Rescorer of Streaming Speech Recognition (2022.findings-emnlp)
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| Challenge: | Recent studies have shown that streaming end-to-end speech recognition models suffer from higher word error rates (WER) compared to non-streaming models, streaming endto-ended ASR models are limited to short audio context or not use future context to satisfy low latency constraints. |
| Approach: | They propose a 2nd-pass rescoring model on top of the 1st-pass streaming model to improve recognition accuracy while keeping latency low. |
| Outcome: | The proposed method improves word error rate significantly compared to the existing model without adding any additional parameters or latency. |
Evaluating Automatic Speech Recognition Systems for Korean Meteorological Experts (2025.findings-emnlp)
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| Challenge: | Using a dataset of Korean weather queries, we find that automatic speech recognition systems fail on specialized vocabulary. |
| Approach: | They propose an evaluation dataset of Korean weather queries . the dataset was recorded by diverse native speakers following pronunciation guidelines . |
| Outcome: | The proposed model reduces error rates on meteorological terms and improves overall recognition accuracy. |
Digitizing Nepal’s Written Heritage: A Comprehensive HTR Pipeline for Old Nepali Manuscripts (2026.acl-long)
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| Challenge: | Using a line-level transcription approach, we explore encoder-decoder architectures and data-centric techniques to improve recognition accuracy for Old Nepali manuscripts. |
| Approach: | They propose a line-level transcription approach and explore encoder-decoder architectures and data-centric techniques to improve recognition accuracy. |
| Outcome: | The proposed model achieves a 4.9% error rate and is highly reliable. |
Evaluation of Manual and Non-manual Components for Sign Language Recognition (2020.lrec-1)
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Medet Mukushev, Arman Sabyrov, Alfarabi Imashev, Kenessary Koishybay, Vadim Kimmelman, Anara Sandygulova
| Challenge: | Deaf communities communicate via sign languages to express meaning and intent. |
| Approach: | They used sign samples from 20 commonly used signs in Kazakh-Russian Sign Language (K-RSL) to investigate whether non-manual components would improve sign’s recognition accuracy. |
| Outcome: | The results showed that using non-manual components would improve sign recognition accuracy. |
GRPO-Guided Modality Selection Enhanced LoRA-Tuned LLMs for Multimodal Emotion Recognition (2025.findings-emnlp)
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| Challenge: | Multimodal emotion recognition in conversation (MERC) aims to identify speakers’ emotional states by utilizing text, audio, and visual modalities. |
| Approach: | They propose an adaptive modality selection framework for multimodal emotion recognition in conversation that integrates all available modalities into one . |
| Outcome: | The proposed framework outperforms existing methods on multimodal dialogue datasets and is available at https://github.com/youflyaway/Modality-Selection-Enhanced-LoRA-Tuned-LLMs. |
The Key Points: Using Feature Importance to Identify Shortcomings in Sign Language Recognition Models (2024.lrec-main)
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| Challenge: | Pose estimation keypoints are widely used in sign language recognition (SLR) but they are difficult to achieve due to the large degree of variability between occurrences of the same sign, the lack of large datasets and the imbalanced nature of the data. |
| Approach: | They propose to use pose estimation keypoints to generalise to unseen signers by identifying potentially redundant features and identifying key points that are most informative to SLR . they propose to train models with large datasets and labelled data to find key points which are redundant to differentiating between signs . |
| Outcome: | The proposed model can be trained on large datasets and has more generalised features than would be possible with a small dataset. |
Improving Handshape Representations for Sign Language Processing: A Graph Neural Network Approach (2025.emnlp-main)
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| Challenge: | Existing systems for sign language recognition process a signing sequence holistically, leaving handshape information implicit, which limits both recognition accuracy and linguistic analysis. |
| Approach: | They propose a graph neural network that separates temporal dynamics from static handshape configurations in continuous signing sequences. |
| Outcome: | The proposed approach achieves 46% accuracy across 37 handshape classes, compared to 25% for baseline methods. |
WkNER: Enhancing Named Entity Recognition with Word Segmentation Constraints and kNN Retrieval (2024.lrec-main)
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| Challenge: | Named Entity Recognition (NER) tasks require detecting the span and category of the entity from the text block. |
| Approach: | They propose a kNN retrieval enhancement algorithm that incorporates word segmentation information to enhance the model’s generalization ability and alleviate the problem of missing entity tokens in prediction. |
| Outcome: | The proposed method improves the performance of baseline models and achieves better or compared recognition accuracy than previous state-of-the-art models in multiple public Chinese and English datasets. |