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 351–375 of 1209 papers

TitleStatusHype
Improving OCR Quality in 19th Century Historical Documents Using a Combined Machine Learning Based Approach—0
An Empirical Study of Scaling Law for Scene Text RecognitionCode2
Efficient Multi-domain Text Recognition Deep Neural Network Parameterization with Residual AdaptersCode0
Bidirectional Trained Tree-Structured Decoder for Handwritten Mathematical Expression Recognition—0
An Empirical Study of Scaling Law for OCRCode1
Chaurah: A Smart Raspberry Pi based Parking System—0
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
When Graph Data Meets Multimodal: A New Paradigm for Graph Understanding and ReasoningCode1
Privacy-Aware Document Visual Question AnsweringCode1
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
Vary: Scaling up the Vision Vocabulary for Large Vision-Language ModelsCode3
Enhancing Vehicle Entrance and Parking Management: Deep Learning Solutions for Efficiency and Security—0
UPOCR: Towards Unified Pixel-Level OCR Interface—0
DocReal: Robust Document Dewarping of Real-Life Images via Attention-Enhanced Control Point PredictionCode1
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
SUT: a new multi-purpose synthetic dataset for Farsi document image analysisCode0
Optimization of Image Processing Algorithms for Character Recognition in Cultural Typewritten DocumentsCode0
Data Generation for Post-OCR correction of Cyrillic handwritingCode1
Similar Document Template Matching Algorithm—0
ChemScraper: Leveraging PDF Graphics Instructions for Molecular Diagram ParsingCode0
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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