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 951–975 of 1209 papers

TitleStatusHype
Optical Character Recognition and Transcription of Berber Signs from Images in a Low-Resource Language Amazigh—0
Optical Character Recognition (OCR) for Telugu: Database, Algorithm and Application—0
Optical character recognition quality affects perceived usefulness of historical newspaper clippings—0
Optical Character Recognition using Convolutional Neural Networks for Ashokan Brahmi Inscriptions—0
Optical Character Recognition, Using K-Nearest Neighbors—0
Optical Character Recognition, Word Segmentation, Sentence Segmentation, and Information Extraction for Historical and Literature Texts in Classical Chinese—0
Optical Text Recognition in Nepali and Bengali: A Transformer-based Approach—0
Optimal Boxes: Boosting End-to-End Scene Text Recognition by Adjusting Annotated Bounding Boxes via Reinforcement Learning—0
Optimizing the Neural Network Training for OCR Error Correction of Historical Hebrew Texts—0
OSPC: Detecting Harmful Memes with Large Language Model as a Catalyst—0
Out-of-Candidate Rectification for Weakly Supervised Semantic Segmentation—0
Out-of-Distribution Recovery with Object-Centric Keypoint Inverse Policy for Visuomotor Imitation Learning—0
Out-of-Vocabulary Challenge Report—0
Overlay Text Extraction From TV News Broadcast—0
Overview of the 2017 ALTA Shared Task: Correcting OCR Errors—0
PACMAN: a framework for pulse oximeter digit detection and reading in a low-resource setting—0
PaddleOCR 3.0 Technical Report—0
Page Stream Segmentation with Convolutional Neural Nets Combining Textual and Visual Features—0
PAM: Understanding Product Images in Cross Product Category Attribute Extraction—0
papago: A Machine Translation Service with Word Sense Disambiguation and Currency Conversion—0
Pay Voice: Point of Sale Recognition for Visually Impaired People—0
PDFdigest: an Adaptable Layout-Aware PDF-to-XML Textual Content Extractor for Scientific Articles—0
PdfTable: A Unified Toolkit for Deep Learning-Based Table Extraction—0
PDF-to-Text Reanalysis for Linguistic Data Mining—0
People and Places of Historical Europe: Bootstrapping Annotation Pipeline and a New Corpus of Named Entities in Late Medieval Texts—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