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
ClusterTabNet: Supervised clustering method for table detection and table structure recognitionCode1
CLEval: Character-Level Evaluation for Text Detection and Recognition TasksCode1
CMULAB: An Open-Source Framework for Training and Deployment of Natural Language Processing ModelsCode1
GenKIE: Robust Generative Multimodal Document Key Information ExtractionCode1
ChroniclingAmericaQA: A Large-scale Question Answering Dataset based on Historical American Newspaper PagesCode1
GenPlot: Increasing the Scale and Diversity of Chart Derendering DataCode1
ChartReader: A Unified Framework for Chart Derendering and Comprehension without Heuristic RulesCode1
CORU: Comprehensive Post-OCR Parsing and Receipt Understanding DatasetCode1
Fused Text Recogniser and Deep Embeddings Improve Word Recognition and RetrievalCode1
Generating Synthetic Handwritten Historical Documents With OCR Constrained GANsCode1
Geometry Restoration and Dewarping of Camera-Captured Document ImagesCode1
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
BROS: A Pre-trained Language Model Focusing on Text and Layout for Better Key Information Extraction from DocumentsCode1
FigStep: Jailbreaking Large Vision-Language Models via Typographic Visual PromptsCode1
Focus, Distinguish, and Prompt: Unleashing CLIP for Efficient and Flexible Scene Text RetrievalCode1
Exploring Better Text Image Translation with Multimodal CodebookCode1
Enhancing License Plate Super-Resolution: A Layout-Aware and Character-Driven ApproachCode1
Exploring Cross-Image Pixel Contrast for Semantic SegmentationCode1
Exploring OCR Capabilities of GPT-4V(ision) : A Quantitative and In-depth EvaluationCode1
Awaker2.5-VL: Stably Scaling MLLMs with Parameter-Efficient Mixture of ExpertsCode1
EAST: An Efficient and Accurate Scene Text DetectorCode1
A Comprehensive Gold Standard and Benchmark for Comics Text Detection and RecognitionCode1
Easter2.0: Improving convolutional models for handwritten text recognitionCode1
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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