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 551–575 of 1209 papers

TitleStatusHype
A Masked Bounding-Box Selection Based ResNet Predictor for Text Rotation Prediction—0
You’ve translated it, now what?—0
A Black-Box Attack on Optical Character Recognition Systems—0
An Energy Activity Dataset for Smart Homes—0
AiM: Taking Answers in Mind to Correct Chinese Cloze Tests in Educational ApplicationsCode0
Effectiveness of Mining Audio and Text Pairs from Public Data for Improving ASR Systems for Low-Resource Languages—0
Visual Subtitle Feature Enhanced Video Outline Generation—0
Graph Neural Networks and Representation Embedding for Table Extraction in PDF DocumentsCode1
An End-to-End OCR Framework for Robust Arabic-Handwriting Recognition using a Novel Transformers-based Model and an Innovative 270 Million-Words Multi-Font Corpus of Classical Arabic with Diacritics—0
To show or not to show: Redacting sensitive text from videos of electronic displays—0
Character decomposition to resolve class imbalance problem in Hangul OCRCode0
Information Extraction from Scanned Invoice Images using Text Analysis and Layout Features—0
Optimal Boxes: Boosting End-to-End Scene Text Recognition by Adjusting Annotated Bounding Boxes via Reinforcement Learning—0
Marior: Margin Removal and Iterative Content Rectification for Document Dewarping in the WildCode1
When Counting Meets HMER: Counting-Aware Network for Handwritten Mathematical Expression RecognitionCode2
You Actually Look Twice At it (YALTAi): using an object detection approach instead of region segmentation within the Kraken engineCode1
Dynamic Low-Resolution Distillation for Cost-Efficient End-to-End Text Spotting—0
DEXTER: An end-to-end system to extract table contents from electronic medical health documents—0
DavarOCR: A Toolbox for OCR and Multi-Modal Document Understanding—0
GMN: Generative Multi-modal Network for Practical Document Information Extraction—0
Towards Multimodal Vision-Language Models Generating Non-Generic Text—0
Detection of Furigana Text in ImagesCode1
BusiNet -- a Light and Fast Text Detection Network for Business Documents—0
Sequence-aware multimodal page classification of Brazilian legal documentsCode0
Challenging America: Modeling language in longer time scales—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