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 526–550 of 1209 papers

TitleStatusHype
DECDM: Document Enhancement using Cycle-Consistent Diffusion Models—0
Automated Parsing of Engineering Drawings for Structured Information Extraction Using a Fine-tuned Document Understanding Transformer—0
IncepText: A New Inception-Text Module with Deformable PSROI Pooling for Multi-Oriented Scene Text Detection—0
Indigenous language technologies in Canada: Assessment, challenges, and successes—0
DavarOCR: A Toolbox for OCR and Multi-Modal Document Understanding—0
Automated Error Detection in Digitized Cultural Heritage Documents—0
Data Cleaning for XML Electronic Dictionaries via Statistical Anomaly Detection—0
Automated data extraction of bar chart raster images—0
Ancient but Digitized: Developing Handwritten Optical Character Recognition for East Syriac Script Through Creating KHAMIS Dataset—0
Indonesian ID Card Extractor Using Optical Character Recognition and Natural Language Post-Processing—0
Information Extraction from Unstructured data using Augmented-AI and Computer Vision—0
Neural Probabilistic System for Text Recognition—0
DanProof: Pedagogical Spell and Grammar Checking for Danish—0
An Assessment of the Impact of OCR Noise on Language Models—0
CSECU\_KDE\_MA at SemEval-2020 Task 8: A Neural Attention Model for Memotion Analysis—0
CryptoDL: Deep Neural Networks over Encrypted Data—0
Autocorrection of arabic common errors for large text corpus—0
Improving OCR Quality in 19th Century Historical Documents Using a Combined Machine Learning Based Approach—0
Crowdsourcing an OCR Gold Standard for a German and French Heritage Corpus—0
Advanced Knowledge Extraction of Physical Design Drawings, Translation and conversion to CAD formats using Deep Learning—0
CREPE: Coordinate-Aware End-to-End Document Parser—0
Improving OCR-Based Image Captioning by Incorporating Geometrical Relationship—0
Improving Optical Character Recognition of Finnish Historical Newspapers with a Combination of Fraktur \& Antiqua Models and Image Preprocessing—0
Corrupted but Not Broken: Understanding and Mitigating the Negative Impacts of Corrupted Data in Visual Instruction Tuning—0
Correction of OCR Word Segmentation Errors in Articles from the ACL Collection through Neural Machine Translation Methods—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