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 501–525 of 1209 papers

TitleStatusHype
Advancements and Challenges in Arabic Optical Character Recognition: A Comprehensive Survey—0
TDeLTA: A Light-weight and Robust Table Detection Method based on Learning Text Arrangement—0
Information Extraction from Unstructured data using Augmented-AI and Computer Vision—0
Polar-Doc: One-Stage Document Dewarping with Multi-Scope Constraints under Polar Representation—0
Multimodal Sentiment Analysis: Perceived vs Induced Sentiments—0
Enhancing Vehicle Entrance and Parking Management: Deep Learning Solutions for Efficiency and Security—0
UPOCR: Towards Unified Pixel-Level OCR Interface—0
Pipeline Enabling Zero-shot Classification for Bangla Handwritten Grapheme—0
Automatic Recognition of Learning Resource Category in a Digital LibraryCode0
Vulnerability Analysis of Transformer-based Optical Character Recognition to Adversarial Attacks—0
Optimization of Image Processing Algorithms for Character Recognition in Cultural Typewritten DocumentsCode0
SUT: a new multi-purpose synthetic dataset for Farsi document image analysisCode0
Similar Document Template Matching Algorithm—0
ChemScraper: Leveraging PDF Graphics Instructions for Molecular Diagram ParsingCode0
DocPedia: Unleashing the Power of Large Multimodal Model in the Frequency Domain for Versatile Document Understanding—0
Efficient End-to-End Visual Document Understanding with Rationale Distillation—0
DECDM: Document Enhancement using Cycle-Consistent Diffusion Models—0
Multiple-Question Multiple-Answer Text-VQA—0
Reading Between the Mud: A Challenging Motorcycle Racer Number DatasetCode0
What Large Language Models Bring to Text-rich VQA?—0
DONUT-hole: DONUT Sparsification by Harnessing Knowledge and Optimizing Learning Efficiency—0
On Manipulating Scene Text in the Wild with Diffusion ModelsCode0
DCQA: Document-Level Chart Question Answering towards Complex Reasoning and Common-Sense UnderstandingCode0
PHD: Pixel-Based Language Modeling of Historical DocumentsCode0
MultiCoNER v2: a Large Multilingual dataset for Fine-grained and Noisy Named Entity Recognition—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