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 451–475 of 1209 papers

TitleStatusHype
Chaurah: A Smart Raspberry Pi based Parking System—0
Finding the Evidence: Localization-aware Answer Prediction for Text Visual Question Answering—0
ChatSchema: A pipeline of extracting structured information with Large Multimodal Models based on schema—0
Arabic Handwritten Document OCR Solution with Binarization and Adaptive Scale Fusion Detection—0
Align Me: A framework to generate Parallel Corpus Using OCRs and Bilingual Dictionaries—0
Finding Names in Trove: Named Entity Recognition for Australian Historical Newspapers—0
Financial Table Extraction in Image Documents—0
Fetch-A-Set: A Large-Scale OCR-Free Benchmark for Historical Document Retrieval—0
Chart-RCNN: Efficient Line Chart Data Extraction from Camera Images—0
Arabic Character Segmentation Using Projection Based Approach with Profile's Amplitude Filter—0
ChartParser: Automatic Chart Parsing for Print-Impaired—0
Fast Search with Poor OCR—0
Fast Implementation of 4-bit Convolutional Neural Networks for Mobile Devices—0
Finite State Approach to the Kazakh Nominal Paradigm—0
ChartMind: A Comprehensive Benchmark for Complex Real-world Multimodal Chart Question Answering—0
FLELex: a graded lexical resource for French foreign learners—0
A Proposal of Automatic Error Correction in Text—0
Eyes Closed, Safety On: Protecting Multimodal LLMs via Image-to-Text Transformation—0
ChartEye: A Deep Learning Framework for Chart Information Extraction—0
ExTTNet: A Deep Learning Algorithm for Extracting Table Texts from Invoice Images—0
Extraction of Line Word Character Segments Directly from Run Length Compressed Printed Text Documents—0
Fooling OCR Systems with Adversarial Text Images—0
FormGym: Doing Paperwork with Agents—0
Fraunhofer SIT at CheckThat! 2023: Mixing Single-Modal Classifiers to Estimate the Check-Worthiness of Multi-Modal Tweets—0
Chart-based Reasoning: Transferring Capabilities from LLMs to VLMs—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