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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
A Benchmark and Dataset for Post-OCR text correction in SanskritCode1
On Web-based Visual Corpus Construction for Visual Document UnderstandingCode1
Unsupervised Audio-Visual Lecture SegmentationCode1
Efficient few-shot learning for pixel-precise handwritten document layout analysis—0
A Late Multi-Modal Fusion Model for Detecting Hybrid Spam E-mail—0
MCSCSet: A Specialist-annotated Dataset for Medical-domain Chinese Spelling CorrectionCode1
OCR-VQGAN: Taming Text-within-Image GenerationCode1
MenuAI: Restaurant Food Recommendation System via a Transformer-based Deep Learning Model—0
Text Detection Forgot About Document OCRCode2
Task Grouping for Multilingual Text RecognitionCode1
Pix2Struct: Screenshot Parsing as Pretraining for Visual Language UnderstandingCode2
Key Information Extraction in Purchase Documents using Deep Learning and Rule-based Corrections—0
EraseNet: A Recurrent Residual Network for Supervised Document Cleaning—0
Chandojnanam: A Sanskrit Meter Identification and Utilization SystemCode0
Synthesizing Annotated Image and Video Data Using a Rendering-Based Pipeline for Improved License Plate Recognition—0
3D Rendering Framework for Data Augmentation in Optical Character Recognition—0
Toward 3D Spatial Reasoning for Human-like Text-based Visual Question Answering—0
HAPI: A Large-scale Longitudinal Dataset of Commercial ML API PredictionsCode1
Out-of-Vocabulary Challenge Report—0
OCR for TIFF Compressed Document Images Directly in Compressed Domain Using Text segmentation and Hidden Markov Model—0
Document Image Binarization in JPEG Compressed Domain using Dual Discriminator Generative Adversarial Networks—0
Computer vision based vehicle tracking as a complementary and scalable approach to RFID tagging—0
PreSTU: Pre-Training for Scene-Text Understanding—0
Multi-Granularity Prediction for Scene Text RecognitionCode0
Levenshtein OCRCode0
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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