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 76–100 of 1209 papers

TitleStatusHype
ChroniclingAmericaQA: A Large-scale Question Answering Dataset based on Historical American Newspaper PagesCode1
German Parliamentary Corpus (GerParCor)Code1
GenKIE: Robust Generative Multimodal Document Key Information ExtractionCode1
ChartReader: A Unified Framework for Chart Derendering and Comprehension without Heuristic RulesCode1
GenPlot: Increasing the Scale and Diversity of Chart Derendering DataCode1
Fused Text Recogniser and Deep Embeddings Improve Word Recognition and RetrievalCode1
FuseCap: Leveraging Large Language Models for Enriched Fused Image CaptionsCode1
Generating Synthetic Handwritten Historical Documents With OCR Constrained GANsCode1
Geometry Restoration and Dewarping of Camera-Captured Document ImagesCode1
Graph Neural Networks and Representation Embedding for Table Extraction in PDF DocumentsCode1
Improving accuracy and speeding up Document Image Classification through parallel systemsCode1
FlowLearn: Evaluating Large Vision-Language Models on Flowchart UnderstandingCode1
FAWA: Fast Adversarial Watermark Attack on Optical Character Recognition (OCR) SystemsCode1
FigStep: Jailbreaking Large Vision-Language Models via Typographic Visual PromptsCode1
Exploring Better Text Image Translation with Multimodal CodebookCode1
Benchmarking Vision-Language Models on Optical Character Recognition in Dynamic Video EnvironmentsCode1
Exploring Cross-Image Pixel Contrast for Semantic SegmentationCode1
BROS: A Pre-trained Language Model Focusing on Text and Layout for Better Key Information Extraction from DocumentsCode1
bbOCR: An Open-source Multi-domain OCR Pipeline for Bengali DocumentsCode1
Exploring OCR Capabilities of GPT-4V(ision) : A Quantitative and In-depth EvaluationCode1
ClusterTabNet: Supervised clustering method for table detection and table structure recognitionCode1
Focus, Distinguish, and Prompt: Unleashing CLIP for Efficient and Flexible Scene Text RetrievalCode1
Easter2.0: Improving convolutional models for handwritten text recognitionCode1
A Comprehensive Gold Standard and Benchmark for Comics Text Detection and RecognitionCode1
EAST: An Efficient and Accurate Scene Text DetectorCode1
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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