Papers Handwritten Text Recognition
“Handwritten Text Recognition” 태그가 달린 논문 171편 · 필터 해제
Measuring Annotation Efficiency for Handwritten Devanagari Recognition: Sample-Complexity Curves for Four Pretraining Regimes
To train handwritten text recognition systems we need word images and their corresponding transcriptions, and these transcriptions are produced manually. For a script that can be read by only a small number of specialist…
Handwritten Text RecognitionOmniHandwritingOCR: A Diagnostic Benchmark for Evaluating Multimodal LLMs in Handwritten OCR Scenarios
Multimodal large language models (MLLMs) are increasingly used as OCR systems in document and knowledge-processing pipelines, but their ability to faithfully read real handwriting remains underexplored. Existing OCR benc…
Handwritten Text RecognitionTrOCR for Medieval HTR: A Systematic Ablation Study with Cross-Dataset Validation
Fine-tuning transformer-based handwritten text recognition (HTR) models on medieval manuscripts is challenging because these models are pre-trained on modern text and must adapt to a very different visual domain. This pa…
Handwritten Text RecognitionData AugmentationPerformance Gap Analysis between Latin and Arabic Scripts HTR
Recent studies have shown that handwritten text recognition (HTR) systems perform worse on Arabic-script datasets than on Latin-script data. However, the reasons for this gap are still not well understood due to the lack…
Handwritten Text RecognitionUrdu Katib Handwritten Dataset: A Historical Document Dataset for Offline Urdu Handwritten Text Recognition with CRNN-Based Baseline Evaluation
Automatic Handwritten Text Recognition (HTR) is inherently a challenging task, and its complexity is further increased when dealing with cursive scripts. Although significant efforts have been made on various cursive scr…
Handwritten Text RecognitionHandwriting RecognitionA Text Recognition Dataset from Sahidic Coptic Ancient Manuscripts
In this work, we target Handwritten Text Recognition (HTR) in low-resource scenarios, which arise from underrepresented languages, rare scripts, and degraded visual conditions typical of historical documents. We introduc…
Handwritten Text RecognitionStringalign: Moving beyond summary statistics with a transparent Unicode-aware tool for evaluating automatic transcription models
Comparing text strings is crucial when evaluating and understanding the performance of various text processing tasks such as document recognition and audio transcription. With an increasingly complex landscape of AI-base…
Handwritten Text RecognitionSpeech RecognitionLeveraging Morphology for Historical Script Metrological Analysis
Advances in handwritten text recognition have enabled large-scale transcription of historical documents, but still provide limited access to interpretable visual measurements for paleography, the study of historical scri…
Handwritten Text RecognitionBullingerDB: A Dataset for Handwritten Text Recognition and Writer Retrieval
We present BullingerDB, a large-scale benchmark dataset for historical document analysis based on the correspondence of Heinrich Bullinger (1504-1575). The corpus comprises 20,898 pages and 499,222 text lines written by …
Handwritten Text RecognitionUnderstanding Cross-Language Transfer Improvements in Low-Resource HTR: The Role of Sequence Modeling
Handwritten Text Recognition (HTR) for Arabic-script languages benefits from cross-language joint training under low-resource conditions, particularly when using CRNN-based models that combine convolutional encoders with…
Handwritten Text RecognitionCross-Lingual Learning within Arabic Script for Low-Resource HTR
Handwritten Text Recognition (HTR) with limited labeled data remains a challenging problem, particularly for Arabic-script languages. Although modern sequence-based recognizers perform well in high-resource settings, the…
Handwritten Text RecognitionZero-Shot Synthetic-to-Real Handwritten Text Recognition via Task Analogies
Handwritten Text Recognition (HTR) models trained on synthetic handwriting often struggle to generalize to real text, and existing adaptation methods still require real samples from the target domain. In this work, we ta…
Handwritten Text RecognitionPoM: A Linear-Time Replacement for Attention with the Polynomial Mixer
This paper introduces the Polynomial Mixer (PoM), a novel token mixing mechanism with linear complexity that serves as a drop-in replacement for self-attention. PoM aggregates input tokens into a compact representation t…
Handwritten Text RecognitionImage GenerationText GenerationFew-shot Writer Adaptation via Multimodal In-Context Learning
While state-of-the-art Handwritten Text Recognition (HTR) models perform well on standard benchmarks, they frequently struggle with writers exhibiting highly specific styles that are underrepresented in the training data…
Handwritten Text RecognitionN-gram Injection into Transformers for Dynamic Language Model Adaptation in Handwritten Text Recognition
Transformer-based encoder-decoder networks have recently achieved impressive results in handwritten text recognition, partly thanks to their auto-regressive decoder which implicitly learns a language model. However, such…
Handwritten Text RecognitionAn Effective Data Augmentation Method by Asking Questions about Scene Text Images
Scene text recognition (STR) and handwritten text recognition (HTR) face significant challenges in accurately transcribing textual content from images into machine-readable formats. Conventional OCR models often predict …
Handwritten Text RecognitionScene Text RecognitionData AugmentationDRetHTR: Linear-Time Decoder-Only Retentive Network for Handwritten Text Recognition
State-of-the-art handwritten text recognition (HTR) systems commonly use Transformers, whose growing key-value (KV) cache makes decoding slow and memory-intensive. We introduce DRetHTR, a decoder-only model built on Rete…
Handwritten Text RecognitionA Human-in-the-Loop Label Error Detection Framework Applied to Arabic-Script HTR Datasets
Despite recent advances, Handwritten Text Recognition (HTR) for Arabic-script languages still lags behind Latin-script HTR. Part of the problem is dataset quality. To help closing this gap, we propose a two-stage framewo…
Handwritten Text RecognitionLabel Error DetectionBeHGAN: Bengali Handwritten Word Generation from Plain Text Using Generative Adversarial Networks
Handwritten Text Recognition (HTR) is a well-established research area. In contrast, Handwritten Text Generation (HTG) is an emerging field with significant potential. This task is challenging due to the variation in ind…
Handwritten Text RecognitionText GenerationApplication of deep learning approaches for medieval historical documents transcription
Handwritten text recognition and optical character recognition solutions show excellent results with processing data of modern era, but efficiency drops with Latin documents of medieval times. This paper presents a deep …
Handwritten Text Recognition