SOTAVerified

Optical Character Recognition (OCR)

Optical Character Recognition or Optical Character Reader (OCR) is the electronic or mechanical conversion of images of typed, handwritten or printed text into machine-encoded text, whether from a scanned document, a photo of a document, a scene-photo (for example the text on signs and billboards in a landscape photo, license plates in cars...) or from subtitle text superimposed on an image (for example: from a television broadcast)

Papers

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Showing 751–775 of 1209 papers

TitleStatusHype
Towards Unified Multi-granularity Text Detection with Interactive Attention—0
Towards Visual Text Design Transfer Across Languages—0
Towards Visual Text Grounding of Multimodal Large Language Model—0
Toward Zero-shot Character Recognition: A Gold Standard Dataset with Radical-level Annotations—0
Tracing Text Provenance via Context-Aware Lexical Substitution—0
Training \& Quality Assessment of an Optical Character Recognition Model for Northern Haida—0
Traitement des Mots Hors Vocabulaire pour la Traduction Automatique de Document OCRis\'es en Arabe (This article presents a new system that automatically translates images of arabic documents)—0
Transfer Learning Approach for Railway Technical Map (RTM) Component Identification—0
Transferring General Multimodal Pretrained Models to Text Recognition—0
Transferring Modern Named Entity Recognition to the Historical Domain: How to Take the Step?—0
Transformer-Based UNet with Multi-Headed Cross-Attention Skip Connections to Eliminate Artifacts in Scanned Documents—0
Transformer based Urdu Handwritten Text Optical Character Reader—0
Transformers Utilization in Chart Understanding: A Review of Recent Advances & Future Trends—0
Transliterated Mobile Keyboard Input via Weighted Finite-State Transducers—0
Tree-Structured Named Entity Recognition on OCR Data: Analysis, Processing and Results—0
TS-Net: OCR Trained to Switch Between Text Transcription Styles—0
Twitter100k: A Real-world Dataset for Weakly Supervised Cross-Media Retrieval—0
Two Decades of Bengali Handwritten Digit Recognition: A Survey—0
Ultra Light OCR Competition Technical Report—0
Unfolding the Past: A Comprehensive Deep Learning Approach to Analyzing Incunabula Pages—0
Unifying Multimodal Retrieval via Document Screenshot Embedding—0
Unitail: Detecting, Reading, and Matching in Retail Scene—0
UNIT: Unifying Image and Text Recognition in One Vision Encoder—0
Unraveling Movie Genres through Cross-Attention Fusion of Bi-Modal Synergy of Poster—0
Unsupervised Code-Switching for Multilingual Historical Document Transcription—0
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All datasetsBenchmarking Chinese Text Recognition: Datasets, Baselines, and an Empirical StudyVideoDB's OCR Benchmark Public CollectionFSNS - TestI2L-140KSUTim2latex-100k

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1DTrOCRAccuracy (%)89.6—Unverified
2DTrOCR 105MAccuracy (%)89.6—Unverified
3MaskOCR-LAccuracy (%)82.6—Unverified
4TransOCRAccuracy (%)72.8—Unverified
5SRNAccuracy (%)65—Unverified
6MORANAccuracy (%)64.3—Unverified
7SEEDAccuracy (%)61.2—Unverified
#ModelMetricClaimedVerifiedStatus
1GPT-4oAverage Accuracy76.22—Unverified
2Gemini-1.5 ProAverage Accuracy76.13—Unverified
3Claude-3 SonnetAverage Accuracy67.71—Unverified
4RapidOCRAverage Accuracy56.98—Unverified
5EasyOCRAverage Accuracy49.3—Unverified
#ModelMetricClaimedVerifiedStatus
1STREETSequence error27.54—Unverified
2SEESequence error22—Unverified
3AttentionOCR_Inception-resnet-v2_LocationSequence error15.8—Unverified
#ModelMetricClaimedVerifiedStatus
1I2L-NOPOOLBLEU89.09—Unverified
2I2L-STRIPSBLEU89—Unverified
#ModelMetricClaimedVerifiedStatus
1TesseractCharacter Error Rate (CER)0.08—Unverified
2EasyOCRCharacter Error Rate (CER)0.07—Unverified
#ModelMetricClaimedVerifiedStatus
1I2L-STRIPSBLEU88.86—Unverified