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 726–750 of 1209 papers

TitleStatusHype
The Solution for the ICCV 2023 1st Scientific Figure Captioning Challenge—0
The System Description of dun_oscar team for The ICPR MSR Challenge—0
TICCLops: Text-Induced Corpus Clean-up as online processing system—0
Tiger200K: Manually Curated High Visual Quality Video Dataset from UGC Platform—0
TokBench: Evaluating Your Visual Tokenizer before Visual Generation—0
Toolbox : une chaîne de traitement de corpus pour les humanités numériques (Toolbox : a corpus processing pipeline for digital humanities)—0
Topic Stability over Noisy Sources—0
To show or not to show: Redacting sensitive text from videos of electronic displays—0
Toward 3D Spatial Reasoning for Human-like Text-based Visual Question Answering—0
Toward a Period-Specific Optimized Neural Network for OCR Error Correction of Historical Hebrew Texts—0
Toward Creation of Ancash Lexical Resources from OCR—0
Towards Accessible Learning: Deep Learning-Based Potential Dysgraphia Detection and OCR for Potentially Dysgraphic Handwriting—0
Towards Accurate Scene Text Recognition with Semantic Reasoning Networks—0
Towards an ACL Anthology Corpus with Logical Document Structure. An Overview of the ACL 2012 Contributed Task—0
Towards an Automatic Classification of Illustrative Examples in a Large Japanese-French Dictionary Obtained by OCR—0
Towards Calibration Enhanced Network by Inverse Adversarial Attack—0
Towards Escaping from Language Bias and OCR Error: Semantics-Centered Text Visual Question Answering—0
Towards Image-based Automatic Meter Reading in Unconstrained Scenarios: A Robust and Efficient Approach—0
Towards Multimodal Vision-Language Models Generating Non-Generic Text—0
Towards Natural Language-Based Document Image Retrieval: New Dataset and Benchmark—0
Towards Optimizing OCR for Accessibility—0
Towards Processing of the Oral History Interviews and Related Printed Documents—0
Towards reducing hallucination in extracting information from financial reports using Large Language Models—0
Towards Robust Handwritten Text Recognition with On-the-fly User Participation—0
Towards Unconstrained End-to-End Text Spotting—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