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 626–650 of 1209 papers

TitleStatusHype
Semi-Structured Query Grounding for Document-Oriented Databases with Deep Retrieval and Its Application to Receipt and POI Matching—0
Identifying OCRs in cfDNA WGS Data by Correlation Clustering—0
BLPnet: A new DNN model and Bengali OCR engine for Automatic License Plate Recognition—0
Omnifont Persian OCR System Using Primitives—0
DocBed: A Multi-Stage OCR Solution for Documents with Complex Layouts—0
Self-paced learning to improve text row detection in historical documents with missing labels—0
An Assessment of the Impact of OCR Noise on Language Models—0
A Classical Approach to Handcrafted Feature Extraction Techniques for Bangla Handwritten Digit Recognition—0
Classroom Slide Narration System—0
Legal Entity Extraction using a Pointer Generator Network—0
Improve Sentence Alignment by Divide-and-conquer—0
On the Cross-dataset Generalization in License Plate RecognitionCode1
SAFL: A Self-Attention Scene Text Recognizer with Focal LossCode0
Intelligent Document Processing -- Methods and Tools in the real world—0
LaTr: Layout-Aware Transformer for Scene-Text VQACode1
Challenging America: Modeling language in longer time scales—0
Lesan -- Machine Translation for Low Resource Languages—0
Tracing Text Provenance via Context-Aware Lexical Substitution—0
Modelling Lips-State Detection Using CNN for Non-Verbal Communications—0
A Survey on Deep learning based Document Image Enhancement—0
An Automatic Approach for Generating Rich, Linked Geo-Metadata from Historical Map ImagesCode1
Transferring Modern Named Entity Recognition to the Historical Domain: How to Take the Step?—0
On-Device Spatial Attention based Sequence Learning Approach for Scene Text Script Identification—0
OCR-free Document Understanding TransformerCode3
Image preprocessing and modified adaptive thresholding for improving OCR—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