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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 851–875 of 1209 papers

TitleStatusHype
Arabic Character Segmentation Using Projection Based Approach with Profile's Amplitude Filter—0
Arabic Handwritten Document OCR Solution with Binarization and Adaptive Scale Fusion Detection—0
A random forest system combination approach for error detection in digital dictionaries—0
Artificial Eye for the Blind—0
Artificial neural networks and fuzzy logic for recognizing alphabet characters and mathematical symbols—0
A Scalable Handwritten Text Recognition System—0
A second-order orientation-contrast stimulus for population-receptive-field-based retinotopic mapping—0
A semi-automatic method for document classification in the shipping industry—0
A Simple and Practical Approach to Improve Misspellings in OCR Text—0
A Single-Shot Arbitrarily-Shaped Text Detector based on Context Attended Multi-Task Learning—0
Asking questions on handwritten document collections—0
A Study of Sindhi Related and Arabic Script Adapted languages Recognition—0
A survey of modern optical character recognition techniques—0
A Survey on Deep learning based Document Image Enhancement—0
A Survey on MLLM-based Visually Rich Document Understanding: Methods, Challenges, and Emerging Trends—0
A Survey on Multimodal Large Language Models—0
A Survey on Optical Character Recognition System—0
A Survey on Vietnamese Document Analysis and Recognition: Challenges and Future Directions—0
A System for Identifying and Exploring Text Repetition in Large Historical Document Corpora—0
Attacking Optical Character Recognition (OCR) Systems with Adversarial Watermarks—0
Autocorrection of arabic common errors for large text corpus—0
Neural Probabilistic System for Text Recognition—0
Automated data extraction of bar chart raster images—0
Automated Error Detection in Digitized Cultural Heritage Documents—0
Automated Parsing of Engineering Drawings for Structured Information Extraction Using a Fine-tuned Document Understanding Transformer—0
Show:102550
← PrevPage 35 of 49Next →
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