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 601–625 of 1209 papers

TitleStatusHype
SAML-QC: a Stochastic Assessment and Machine Learning based QC technique for Industrial Printing—0
SARD: A Large-Scale Synthetic Arabic OCR Dataset for Book-Style Text Recognition—0
Scalable Video-to-Dataset Generation for Cross-Platform Mobile Agents—0
Scaling Automatic Extraction of Pseudocode—0
Scatteract: Automated extraction of data from scatter plots—0
SceneGATE: Scene-Graph based co-Attention networks for TExt visual question answering—0
Scene Text recognition with Full Normalization—0
SceneVTG++: Controllable Multilingual Visual Text Generation in the Wild—0
SciCapenter: Supporting Caption Composition for Scientific Figures with Machine-Generated Captions and Ratings—0
Seeing is Believing? Mitigating OCR Hallucinations in Multimodal Large Language Models—0
Seeing the Signs: A Survey of Edge-Deployable OCR Models for Billboard Visibility Analysis—0
See then Tell: Enhancing Key Information Extraction with Vision Grounding—0
SEE: Towards Semi-SupervisedEnd-to-End Scene Text Recognition—0
Segmentation-free Connectionist Temporal Classification loss based OCR Model for Text Captcha Classification—0
Self-paced learning to improve text row detection in historical documents with missing labels—0
Self-supervised Data Bootstrapping for Deep Optical Character Recognition of Identity Documents—0
Semantic rule Web-based Diagnosis and Treatment of Vector-Borne Diseases using SWRL rules—0
Semantic Segmentation for Real-World and Synthetic Vehicle's Forward-Facing Camera Images—0
Semi-automated annotation of page-based documents within the Genre and Multimodality framework—0
Semi-Structured Query Grounding for Document-Oriented Databases with Deep Retrieval and Its Application to Receipt and POI Matching—0
Sequence-to-Label Script Identification for Multilingual OCR—0
Sequence to Sequence Learning for Optical Character Recognition—0
Sequence-to-Sequence Pre-training with Unified Modality Masking for Visual Document Understanding—0
Shakti-VLMs: Scalable Vision-Language Models for Enterprise AI—0
Similar Document Template Matching Algorithm—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
1DTrOCR 105MAccuracy (%)89.6—Unverified
2DTrOCRAccuracy (%)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