DocTTT: Test-Time Training for Handwritten Document Recognition Using Meta-Auxiliary Learning | Paper Citation Record

Observation bd32c61f-47c9-4799-8099-7b12b7c9176a · outbound

This paper cites Improved handwritten digit recognition using convolutional neural networks (cnn).

DocTTT: Test-Time Training for Handwritten Document Recognition Using Meta-Auxiliary Learning Improved handwritten digit recognition using convolutional neural networks (cnn)

Reference 1

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Observation 2cf69210-675e-450f-9d21-a2e78e11e73a · outbound

This paper cites An improved faster-rcnn model for handwritten character recognition.

DocTTT: Test-Time Training for Handwritten Document Recognition Using Meta-Auxiliary Learning An improved faster-rcnn model for handwritten character recognition

Reference 2

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Observation 322a1d6e-4399-4e91-af51-4cca0bee211c · outbound

This paper cites Anderson and Sofia I.

DocTTT: Test-Time Training for Handwritten Document Recognition Using Meta-Auxiliary Learning Anderson and Sofia I

Reference 3

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Observation 0c43adf2-89ed-480a-932c-7344ea09f5bd · outbound

This paper cites Mt3: Meta test-time training for self- supervised test-time adaption.

DocTTT: Test-Time Training for Handwritten Document Recognition Using Meta-Auxiliary Learning Mt3: Meta test-time training for self- supervised test-time adaption

Reference 4

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Observation 036e7993-49a9-48c4-a7f5-6f6573bf7c45 · outbound

This paper cites Docsynth: a layout guided approach for controllable docu- ment image synthesis.

DocTTT: Test-Time Training for Handwritten Document Recognition Using Meta-Auxiliary Learning Docsynth: a layout guided approach for controllable docu- ment image synthesis

Reference 5

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Observation a4558f07-ba41-417a-8a8b-5cb0781c1da2 · outbound

This paper cites Boosting modern and historical handwrit- ten text recognition with deformable convolutions.

DocTTT: Test-Time Training for Handwritten Document Recognition Using Meta-Auxiliary Learning Boosting modern and historical handwrit- ten text recognition with deformable convolutions

Reference 6

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Observation 67e53881-a411-4e40-95e1-b3cd48643cbf · outbound

This paper cites Easter2.0: Improving convolutional models for handwritten text recognition.

DocTTT: Test-Time Training for Handwritten Document Recognition Using Meta-Auxiliary Learning Easter2.0: Improving convolutional models for handwritten text recognition

Reference 7

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Observation c878ef53-8667-4397-847f-84c575bb45a4 · outbound

This paper cites Contrastive test-time adaptation.

DocTTT: Test-Time Training for Handwritten Document Recognition Using Meta-Auxiliary Learning Contrastive test-time adaptation

Reference 8

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Observation d916ef07-130b-425f-8f47-67616f9bb446 · outbound

This paper cites Improved test-time adaptation for domain generalization.

DocTTT: Test-Time Training for Handwritten Document Recognition Using Meta-Auxiliary Learning Improved test-time adaptation for domain generalization

Reference 9

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Observation 2a03f9c2-2c85-4a57-bcc3-dcd66b2bbd43 · outbound

This paper cites Metafscil: A meta-learning approach for few-shot class incremental learning.

DocTTT: Test-Time Training for Handwritten Document Recognition Using Meta-Auxiliary Learning Metafscil: A meta-learning approach for few-shot class incremental learning

Reference 10

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Observation 53faa9e7-e428-4590-84ae-114948821528 · outbound

This paper cites Adapting to Distribution Shift by Visual Domain Prompt Generation.

DocTTT: Test-Time Training for Handwritten Document Recognition Using Meta-Auxiliary Learning Adapting to Distribution Shift by Visual Domain Prompt Generation

Reference 11

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Observation 707c59a1-ec30-4b7b-99c0-4d02723d6dd8 · outbound

This paper cites Test- time fast adaptation for dynamic scene deblurring via meta- auxiliary learning.

DocTTT: Test-Time Training for Handwritten Document Recognition Using Meta-Auxiliary Learning Test- time fast adaptation for dynamic scene deblurring via meta- auxiliary learning

Reference 12

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Observation 14d76fc1-42ba-45f8-a49d-c4c67ad1a8ca · outbound

This paper cites Span: a simple predict & align network for handwritten para- graph recognition.

DocTTT: Test-Time Training for Handwritten Document Recognition Using Meta-Auxiliary Learning Span: a simple predict & align network for handwritten para- graph recognition

Reference 13

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Observation c3407b0b-d997-4386-9ee9-f4cbfc810952 · outbound

This paper cites End-to-end handwritten paragraph text recognition using a vertical attention network.

DocTTT: Test-Time Training for Handwritten Document Recognition Using Meta-Auxiliary Learning End-to-end handwritten paragraph text recognition using a vertical attention network

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:45:00.373784Z

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Observation f37678a6-30af-40d0-8718-505ae0ae82bc · outbound

This paper cites Dan: a segmentation-free document attention network for handwritten document recognition.

DocTTT: Test-Time Training for Handwritten Document Recognition Using Meta-Auxiliary Learning Dan: a segmentation-free document attention network for handwritten document recognition

Reference 15

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Observation ffe84d4f-d27e-4e43-90e9-726d5247a434 · outbound

This paper cites Faster dan: Multi-target queries with document positional encoding for end-to-end handwritten document recognition.

DocTTT: Test-Time Training for Handwritten Document Recognition Using Meta-Auxiliary Learning Faster dan: Multi-target queries with document positional encoding for end-to-end handwritten document recognition

Reference 16

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Observation 5b6b401b-a7b8-468f-ab21-2cdf934404e9 · outbound

This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

DocTTT: Test-Time Training for Handwritten Document Recognition Using Meta-Auxiliary Learning BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 17

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Observation c78a2a13-8ef9-438b-8c2b-f23e74f009eb · outbound

This paper cites Improving cnn-rnn hybrid networks for handwrit- ing recognition.

DocTTT: Test-Time Training for Handwritten Document Recognition Using Meta-Auxiliary Learning Improving cnn-rnn hybrid networks for handwrit- ing recognition

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:45:00.336594Z

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Observation cb48cc5d-43e5-4f70-8129-57ba61539bbe · outbound

This paper cites The pascal visual object classes (voc) challenge.

DocTTT: Test-Time Training for Handwritten Document Recognition Using Meta-Auxiliary Learning The pascal visual object classes (voc) challenge

Reference 19

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Observation f6b75cb4-586f-47ef-b1f7-80eb725b1e88 · outbound

This paper cites Model- agnostic meta-learning for fast adaptation of deep networks.

DocTTT: Test-Time Training for Handwritten Document Recognition Using Meta-Auxiliary Learning Model- agnostic meta-learning for fast adaptation of deep networks

Reference 20

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Observation 8ec811a0-40c6-4290-9499-36ba699c990f · outbound

This paper cites Convolutional neural network based intelligent handwritten document recognition.

DocTTT: Test-Time Training for Handwritten Document Recognition Using Meta-Auxiliary Learning Convolutional neural network based intelligent handwritten document recognition

Reference 21

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Observation d484e04f-24a5-46ce-b407-d6a5df737a3d · outbound

This paper cites Handwritten gujarati numer- als classification based on deep convolution neural networks using transfer learning scenarios.

DocTTT: Test-Time Training for Handwritten Document Recognition Using Meta-Auxiliary Learning Handwritten gujarati numer- als classification based on deep convolution neural networks using transfer learning scenarios

Reference 22

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Observation 3db69fd4-b75b-488f-abc5-ef518c836a06 · outbound

This paper cites Icdar 2011-french handwriting recognition competition.

DocTTT: Test-Time Training for Handwritten Document Recognition Using Meta-Auxiliary Learning Icdar 2011-french handwriting recognition competition

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:45:00.278191Z

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Observation b444b35e-12ee-4780-afad-bc8742073eca · outbound

This paper cites Improving ProtoNet for Few-Shot Video Object Recognition: Winner of ORBIT Challenge 2022.

DocTTT: Test-Time Training for Handwritten Document Recognition Using Meta-Auxiliary Learning Improving ProtoNet for Few-Shot Video Object Recognition: Winner of ORBIT Challenge 2022

Reference 24

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Observation 4223121f-08d5-4bd1-8acd-b7234d52b6d8 · outbound

This paper cites Masked autoencoders are scalable vision learners.

DocTTT: Test-Time Training for Handwritten Document Recognition Using Meta-Auxiliary Learning Masked autoencoders are scalable vision learners

Reference 25

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Observation bcaa112b-0efe-4db4-a99d-6c67549e85fe · outbound

This paper cites General models for handwritten text recognition: Feasibility and state-of-the art.

DocTTT: Test-Time Training for Handwritten Document Recognition Using Meta-Auxiliary Learning General models for handwritten text recognition: Feasibility and state-of-the art

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:45:00.252990Z

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Observation 1ae76c83-d0c8-46f8-bd6f-af63ad44d5e1 · outbound

This paper cites Johnson and Emily D.

DocTTT: Test-Time Training for Handwritten Document Recognition Using Meta-Auxiliary Learning Johnson and Emily D

Reference 27

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Observation c095eceb-8784-4dde-b273-222a4aa8367a · outbound

This paper cites Pay Attention to What You Read: Non-recurrent Handwritten Text-Line Recognition.

DocTTT: Test-Time Training for Handwritten Document Recognition Using Meta-Auxiliary Learning Pay Attention to What You Read: Non-recurrent Handwritten Text-Line Recognition

Reference 28

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Observation 33cb8c74-45c5-4de4-96f0-1846b692221d · outbound

This paper cites Meta- sgd: Learning to learn quickly for few-shot learning.

DocTTT: Test-Time Training for Handwritten Document Recognition Using Meta-Auxiliary Learning Meta- sgd: Learning to learn quickly for few-shot learning

Reference 29

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Observation 3bcf6699-ee24-404e-9519-f77b4378a705 · outbound

This paper cites Self-supervised spa- tiotemporal representation learning by exploiting video con- tinuity.

DocTTT: Test-Time Training for Handwritten Document Recognition Using Meta-Auxiliary Learning Self-supervised spa- tiotemporal representation learning by exploiting video con- tinuity

Reference 30

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Observation cb655c92-5c89-40a8-8f34-34ac6e34f4f3 · outbound

This paper cites Microsoft coco: Common objects in context.

DocTTT: Test-Time Training for Handwritten Document Recognition Using Meta-Auxiliary Learning Microsoft coco: Common objects in context

Reference 31

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This paper cites Meta-auxiliary learning for future depth pre- diction in videos.

DocTTT: Test-Time Training for Handwritten Document Recognition Using Meta-Auxiliary Learning Meta-auxiliary learning for future depth pre- diction in videos

Reference 32

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Observation d8689b38-fa77-44d0-b98d-77fc0a14a85b · outbound

This paper cites Few-shot class-incremental learning via entropy-regularized data-free replay.

DocTTT: Test-Time Training for Handwritten Document Recognition Using Meta-Auxiliary Learning Few-shot class-incremental learning via entropy-regularized data-free replay

Reference 33

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Observation 2e2a8860-de77-467b-8e61-5ebc039d2c9d · outbound

This paper cites MaskOCR: Text Recognition with Masked Encoder-Decoder Pretraining.

DocTTT: Test-Time Training for Handwritten Document Recognition Using Meta-Auxiliary Learning MaskOCR: Text Recognition with Masked Encoder-Decoder Pretraining

Reference 34

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Observation c23f2de1-3aae-4b35-932d-9daad32460d5 · outbound

This paper cites Crowd counting us- ing meta-test-time adaptation.

DocTTT: Test-Time Training for Handwritten Document Recognition Using Meta-Auxiliary Learning Crowd counting us- ing meta-test-time adaptation

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:45:00.172547Z

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Observation 9d009277-64c0-4145-b992-a6bbdc7000ad · outbound

This paper cites The iam-database: an english sentence database for offline handwriting recognition.

DocTTT: Test-Time Training for Handwritten Document Recognition Using Meta-Auxiliary Learning The iam-database: an english sentence database for offline handwriting recognition

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:45:00.159321Z

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Observation a4b8eb51-d412-456c-a58c-3960ad8515fe · outbound

This paper cites On First-Order Meta-Learning Algorithms.

DocTTT: Test-Time Training for Handwritten Document Recognition Using Meta-Auxiliary Learning On First-Order Meta-Learning Algorithms

Reference 37

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This paper cites Handwritten kazakh and russian (hkr) database for text recognition.

DocTTT: Test-Time Training for Handwritten Document Recognition Using Meta-Auxiliary Learning Handwritten kazakh and russian (hkr) database for text recognition

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:45:00.145389Z

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Observation 4e049c93-e6d0-43d5-a88d-9315bb4492a4 · outbound

This paper cites Meta-learning of pooling layers for character recognition.

DocTTT: Test-Time Training for Handwritten Document Recognition Using Meta-Auxiliary Learning Meta-learning of pooling layers for character recognition

Reference 39

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Observation 46e278a8-ed22-42d1-9596-d3599189806f · outbound

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DocTTT: Test-Time Training for Handwritten Document Recognition Using Meta-Auxiliary Learning Unresolved cited work

Reference 40

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Observation 55aff686-28f8-4ae3-9e44-3c0f852ce873 · outbound

This paper cites Meta self- learning for multi-source domain adaptation: a benchmark.

DocTTT: Test-Time Training for Handwritten Document Recognition Using Meta-Auxiliary Learning Meta self- learning for multi-source domain adaptation: a benchmark

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:45:00.102832Z

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Observation 30f1eda5-f5be-48d7-97bf-52d93c459956 · outbound

This paper cites Pat- tern recognition-recognition of handwritten document using convolutional neural networks.

DocTTT: Test-Time Training for Handwritten Document Recognition Using Meta-Auxiliary Learning Pat- tern recognition-recognition of handwritten document using convolutional neural networks

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:45:00.088549Z

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Observation 696cdc02-fb16-4673-b100-57114f673406 · outbound

This paper cites Transformer-based approach for joint handwriting and named entity recognition in historical doc- ument.

DocTTT: Test-Time Training for Handwritten Document Recognition Using Meta-Auxiliary Learning Transformer-based approach for joint handwriting and named entity recognition in historical doc- ument

Reference 43

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Observation adcac9b2-274e-4b1b-a4f9-c8d82598e686 · outbound

This paper cites Icfhr2016 competition on hand- written text recognition on the read dataset.

DocTTT: Test-Time Training for Handwritten Document Recognition Using Meta-Auxiliary Learning Icfhr2016 competition on hand- written text recognition on the read dataset

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:45:00.067910Z

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Observation 818e5067-19ad-4c7f-87e8-d8cbda29799b · outbound

This paper cites Image quality assessment through fsim, ssim, mse and psnr—a comparative study.

DocTTT: Test-Time Training for Handwritten Document Recognition Using Meta-Auxiliary Learning Image quality assessment through fsim, ssim, mse and psnr—a comparative study

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:45:00.055404Z

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Observation 04246d05-e979-4c12-95bf-126ca15bd6e0 · outbound

This paper cites Psnr vs ssim: impercepti- bility quality assessment for image steganography.

DocTTT: Test-Time Training for Handwritten Document Recognition Using Meta-Auxiliary Learning Psnr vs ssim: impercepti- bility quality assessment for image steganography

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:45:00.041197Z

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Observation d46815ff-28fd-47f8-838f-c707b65a1e2e · outbound

This paper cites Full page handwriting recognition via image to sequence extraction.

DocTTT: Test-Time Training for Handwritten Document Recognition Using Meta-Auxiliary Learning Full page handwriting recognition via image to sequence extraction

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:45:00.028284Z

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Observation a7a3d541-94c0-49c6-93f1-37e40bb68baf · outbound

This paper cites Test-time training with self- supervision for generalization under distribution shifts.

DocTTT: Test-Time Training for Handwritten Document Recognition Using Meta-Auxiliary Learning Test-time training with self- supervision for generalization under distribution shifts

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-10T16:44:59.733936Z

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This paper cites Attention is all you need.

DocTTT: Test-Time Training for Handwritten Document Recognition Using Meta-Auxiliary Learning Attention is all you need

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-10T16:44:59.737874Z

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Observation 1185fe66-e1af-477e-a7a7-365aa8f0e4c7 · outbound

This paper cites Tent: Fully Test-time Adaptation by Entropy Minimization.

DocTTT: Test-Time Training for Handwritten Document Recognition Using Meta-Auxiliary Learning Tent: Fully Test-time Adaptation by Entropy Minimization

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-10T16:44:59.741517Z

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Observation fd5af7ed-0f14-4bd6-8543-55ab8611d05c · outbound

This paper cites Combinatorial learning of graph edit distance via dynamic embedding.

DocTTT: Test-Time Training for Handwritten Document Recognition Using Meta-Auxiliary Learning Combinatorial learning of graph edit distance via dynamic embedding

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:44:59.988650Z

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Observation e88a87ee-4756-476f-9d25-3ab60c5bb9d6 · outbound

This paper cites Decoupled attention network for text recognition.

DocTTT: Test-Time Training for Handwritten Document Recognition Using Meta-Auxiliary Learning Decoupled attention network for text recognition

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:44:59.976151Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation ec662089-faf4-4a8d-ad65-a6cf1b0e61c4 · outbound

This paper cites Distribution align- ment for fully test-time adaptation with dynamic online data streams.

DocTTT: Test-Time Training for Handwritten Document Recognition Using Meta-Auxiliary Learning Distribution align- ment for fully test-time adaptation with dynamic online data streams

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:44:59.963643Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation aaaaa53c-5920-4af7-96b1-e3edc4a117ae · outbound

This paper cites Start, follow, read: End-to-end full-page handwriting recognition.

DocTTT: Test-Time Training for Handwritten Document Recognition Using Meta-Auxiliary Learning Start, follow, read: End-to-end full-page handwriting recognition

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-10T16:44:59.755859Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2cddc07f-a6e0-415b-a2a3-746ca1d1fdde · outbound

This paper cites Williams and Neha S.

DocTTT: Test-Time Training for Handwritten Document Recognition Using Meta-Auxiliary Learning Williams and Neha S

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-10T16:44:59.759026Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c57254b3-cc89-4fd2-a723-0a2929adc831 · outbound

This paper cites Metagcd: Learning to continually learn in generalized cat- egory discovery.

DocTTT: Test-Time Training for Handwritten Document Recognition Using Meta-Auxiliary Learning Metagcd: Learning to continually learn in generalized cat- egory discovery

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-10T16:44:59.762609Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 166df035-09b5-423f-be5b-c413faa2465a · outbound

This paper cites Test-time domain adaptation by learning domain-aware batch normalization.

DocTTT: Test-Time Training for Handwritten Document Recognition Using Meta-Auxiliary Learning Test-time domain adaptation by learning domain-aware batch normalization

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:44:59.931036Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation b364de14-e844-4ab1-8583-dcb26f95ed75 · outbound

This paper cites A-vit: Adaptive to- kens for efficient vision transformer.

DocTTT: Test-Time Training for Handwritten Document Recognition Using Meta-Auxiliary Learning A-vit: Adaptive to- kens for efficient vision transformer

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:44:59.919646Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 63c46aae-a24c-4c7c-88f3-fd246214edd0 · outbound

This paper cites Meta-dmoe: Adapting to domain shift by meta- distillation from mixture-of-experts.

DocTTT: Test-Time Training for Handwritten Document Recognition Using Meta-Auxiliary Learning Meta-dmoe: Adapting to domain shift by meta- distillation from mixture-of-experts

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:44:59.907092Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers