HAND: Hierarchical Attention Network for Multi-Scale Handwritten Document Recognition and Layout Analysis | Paper Citation Record

Observation 2d153cb7-1ee7-49b9-b40e-7146d82d56b9 · outbound

This paper cites Historical word-spotting in handwritten documents: The challenges,.

HAND: Hierarchical Attention Network for Multi-Scale Handwritten Document Recognition and Layout Analysis Historical word-spotting in handwritten documents: The challenges,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T01:04:47.829329Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation f72169be-f01a-4bb8-804e-bbac0f962e4e · outbound

This paper cites A transformer-based neural network architecture for handwritten document analysis,.

HAND: Hierarchical Attention Network for Multi-Scale Handwritten Document Recognition and Layout Analysis A transformer-based neural network architecture for handwritten document analysis,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T01:04:47.818276Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 8736bf72-1574-437d-927e-670fa1ea71bb · outbound

This paper cites Layout analysis for arabic historical document images using machine learning,.

HAND: Hierarchical Attention Network for Multi-Scale Handwritten Document Recognition and Layout Analysis Layout analysis for arabic historical document images using machine learning,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T01:04:47.806923Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation ae890eaa-81af-4f42-8074-ecddc33d97c3 · outbound

This paper cites Acomprehen- sivesurveyofmostlytextualdocumentsegmentationalgorithms since 2008,.

HAND: Hierarchical Attention Network for Multi-Scale Handwritten Document Recognition and Layout Analysis Acomprehen- sivesurveyofmostlytextualdocumentsegmentationalgorithms since 2008,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T01:04:47.795910Z

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Observation db91e119-d567-435d-aa3c-62d656396971 · outbound

This paper cites A survey on handwritten document understanding technique,.

HAND: Hierarchical Attention Network for Multi-Scale Handwritten Document Recognition and Layout Analysis A survey on handwritten document understanding technique,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T01:04:47.784746Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 024c33b7-d368-427e-ad88-22eed7818f0f · outbound

This paper cites Efficient text line segmentation for historical documents,.

HAND: Hierarchical Attention Network for Multi-Scale Handwritten Document Recognition and Layout Analysis Efficient text line segmentation for historical documents,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T01:04:47.771323Z

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Observation 35c1144b-be8c-4ad9-a97d-b63ccc92b2e0 · outbound

This paper cites End-to-end handwritten text recognition and word spotting with deep neural networks,.

HAND: Hierarchical Attention Network for Multi-Scale Handwritten Document Recognition and Layout Analysis End-to-end handwritten text recognition and word spotting with deep neural networks,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T01:04:47.758008Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 0a792ae9-54e5-4f8b-8212-08a2d8ccc6d1 · outbound

This paper cites Recognition of historical documents with few labeled samples,.

HAND: Hierarchical Attention Network for Multi-Scale Handwritten Document Recognition and Layout Analysis Recognition of historical documents with few labeled samples,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T01:04:47.745614Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation b99f8d27-8077-4e24-8bb3-f2dadab07ff4 · outbound

This paper cites DAN: A segmentation-free document attention network for handwritten document recognition,.

HAND: Hierarchical Attention Network for Multi-Scale Handwritten Document Recognition and Layout Analysis DAN: A segmentation-free document attention network for handwritten document recognition,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T01:04:47.731367Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 8ada38bc-b3c3-4d61-855b-8b8b106950c2 · outbound

This paper cites Faster DAN: Multi-target Queries with Document Positional Encoding for End-to-end Handwritten Document Recognition.

HAND: Hierarchical Attention Network for Multi-Scale Handwritten Document Recognition and Layout Analysis Faster DAN: Multi-target Queries with Document Positional Encoding for End-to-end Handwritten Document Recognition

Reference 10

Resolution: verified exact; local_arxiv, observed 2026-08-11T01:04:47.055673Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 769b672c-712b-43cd-bd6e-157961d482d4 · outbound

This paper cites DANCER: A compu- tationally efficient end-to-end model for handwritten document recognition,.

HAND: Hierarchical Attention Network for Multi-Scale Handwritten Document Recognition and Layout Analysis DANCER: A compu- tationally efficient end-to-end model for handwritten document recognition,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T01:04:47.719276Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 81eab1fc-9c7a-4bbb-89c3-8fc25f70a557 · outbound

This paper cites Pay attention to what youread: Non-recurrent handwritten text-line recognition,.

HAND: Hierarchical Attention Network for Multi-Scale Handwritten Document Recognition and Layout Analysis Pay attention to what youread: Non-recurrent handwritten text-line recognition,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T01:04:47.707418Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 4473ac77-2639-40c7-9488-0963be1193c9 · outbound

This paper cites ollner, and T. Gr.

HAND: Hierarchical Attention Network for Multi-Scale Handwritten Document Recognition and Layout Analysis ollner, and T. Gr

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T01:04:47.697329Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation cb3c600b-76d9-48c1-88a3-70e649d613cd · outbound

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

HAND: Hierarchical Attention Network for Multi-Scale Handwritten Document Recognition and Layout Analysis Transformer-based approach for joint handwriting and named entity recognition in historical documents,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T01:04:47.686229Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 9437de66-502b-421e-96f5-b976962444ee · outbound

This paper cites Resnest-transformer: Joint at- tentionsegmentation-freeforend-to-endhandwritingparagraph recognition model,.

HAND: Hierarchical Attention Network for Multi-Scale Handwritten Document Recognition and Layout Analysis Resnest-transformer: Joint at- tentionsegmentation-freeforend-to-endhandwritingparagraph recognition model,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T01:04:47.674977Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 3bac41ad-bb89-43a8-97eb-6a1e068f4234 · outbound

This paper cites On the computational complexity of self-attention,.

HAND: Hierarchical Attention Network for Multi-Scale Handwritten Document Recognition and Layout Analysis On the computational complexity of self-attention,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T01:04:47.662812Z

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Observation 17a79560-4f49-485f-9a25-0e2de7faf08c · outbound

This paper cites A practical survey on faster and lighter transformers,.

HAND: Hierarchical Attention Network for Multi-Scale Handwritten Document Recognition and Layout Analysis A practical survey on faster and lighter transformers,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T01:04:47.651232Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 8d7d479c-af3c-4c25-8c7e-9283ac3f7c02 · outbound

This paper cites Handwriting recognition with large multidimensional long short-term memory recurrent neural networks,.

HAND: Hierarchical Attention Network for Multi-Scale Handwritten Document Recognition and Layout Analysis Handwriting recognition with large multidimensional long short-term memory recurrent neural networks,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T01:04:47.640743Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 52ec29d1-c337-4b15-86f8-e5ffc49b8de1 · outbound

This paper cites Jointlinesegmentationandtranscriptionforend-to- end handwritten paragraph recognition,.

HAND: Hierarchical Attention Network for Multi-Scale Handwritten Document Recognition and Layout Analysis Jointlinesegmentationandtranscriptionforend-to- end handwritten paragraph recognition,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T01:04:47.626655Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation e5929dbc-f92c-473c-872d-6e2e75ed778b · outbound

This paper cites Data augmentation for recognition of handwritten words and lines using a cnn-lstm network,.

HAND: Hierarchical Attention Network for Multi-Scale Handwritten Document Recognition and Layout Analysis Data augmentation for recognition of handwritten words and lines using a cnn-lstm network,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T01:04:47.615900Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 68a2a9b4-91e8-4dbb-b1be-2bb123f904ef · outbound

This paper cites Scan, attend and read: End-to-end handwritten paragraph recognition with mdl- stm attention,.

HAND: Hierarchical Attention Network for Multi-Scale Handwritten Document Recognition and Layout Analysis Scan, attend and read: End-to-end handwritten paragraph recognition with mdl- stm attention,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T01:04:47.605676Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 2df31e2d-bfde-4b9f-a389-770787aea3e8 · outbound

This paper cites Have convolutions already made recurrence obsolete for uncon- strained handwritten text recognition?.

HAND: Hierarchical Attention Network for Multi-Scale Handwritten Document Recognition and Layout Analysis Have convolutions already made recurrence obsolete for uncon- strained handwritten text recognition?

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T01:04:47.594720Z

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Observation 4cf9f5fa-32de-4b85-a1f5-fda3cd3c8a46 · outbound

This paper cites uning, and J. Z.

HAND: Hierarchical Attention Network for Multi-Scale Handwritten Document Recognition and Layout Analysis uning, and J. Z

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T01:04:47.584180Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation b281a47b-9ef7-424c-8401-65cd4dc0feae · outbound

This paper cites Recurrence-free unconstrained handwritten text recognition using gated fully convolutional network,.

HAND: Hierarchical Attention Network for Multi-Scale Handwritten Document Recognition and Layout Analysis Recurrence-free unconstrained handwritten text recognition using gated fully convolutional network,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T01:04:47.573664Z

Source-reported events for the cited work

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Observation 8e7f48a6-d1de-453c-b22b-f7c10753ed86 · outbound

This paper cites Origaminet: Weakly-supervised, segmentation-free, one-step, full page text recognition by learn- ing to unfold,.

HAND: Hierarchical Attention Network for Multi-Scale Handwritten Document Recognition and Layout Analysis Origaminet: Weakly-supervised, segmentation-free, one-step, full page text recognition by learn- ing to unfold,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T01:04:47.562438Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 083a522b-f88d-4847-abc8-0065526ea0bb · outbound

This paper cites TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models.

HAND: Hierarchical Attention Network for Multi-Scale Handwritten Document Recognition and Layout Analysis TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-11T01:04:46.694685Z

Source-reported events for the cited work

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Observation 74fdd7b2-d3df-49cd-86ca-72f84de00464 · outbound

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

HAND: Hierarchical Attention Network for Multi-Scale Handwritten Document Recognition and Layout Analysis Full page handwriting recognition via image to sequence extraction,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T01:04:47.553197Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 6f64c97e-6e50-4f69-963a-1b08548eb99c · outbound

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

HAND: Hierarchical Attention Network for Multi-Scale Handwritten Document Recognition and Layout Analysis End-to-end hand- written paragraph text recognition using a vertical attention network,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T01:04:47.543483Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation b586e51f-9f92-45c1-82a6-e35a467ef88c · outbound

This paper cites Refocus attention span networks for handwriting line recognition,.

HAND: Hierarchical Attention Network for Multi-Scale Handwritten Document Recognition and Layout Analysis Refocus attention span networks for handwriting line recognition,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T01:04:47.534413Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation d2d6d1b3-78eb-4cff-983b-d46996913c68 · outbound

This paper cites Span: A simple predict and align network for handwritten paragraph recogni- tion,.

HAND: Hierarchical Attention Network for Multi-Scale Handwritten Document Recognition and Layout Analysis Span: A simple predict and align network for handwritten paragraph recogni- tion,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T01:04:47.524334Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 96bc0b96-0759-4115-ba64-3f0fa6f77e52 · outbound

This paper cites Connectionist temporal classification: labelling unsegmented sequence data with recurrent neural networks,.

HAND: Hierarchical Attention Network for Multi-Scale Handwritten Document Recognition and Layout Analysis Connectionist temporal classification: labelling unsegmented sequence data with recurrent neural networks,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T01:04:47.512181Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 918c827a-ef8f-421d-904a-9dd1cb1df82e · outbound

This paper cites Recent progress in deep learning for historical document processing,.

HAND: Hierarchical Attention Network for Multi-Scale Handwritten Document Recognition and Layout Analysis Recent progress in deep learning for historical document processing,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T01:04:47.501782Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation e7f3ec34-af24-4ebd-a89a-9ae8e741a639 · outbound

This paper cites Document layout analysis: A comprehensive survey,.

HAND: Hierarchical Attention Network for Multi-Scale Handwritten Document Recognition and Layout Analysis Document layout analysis: A comprehensive survey,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T01:04:47.490867Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation a2cd7802-c2eb-4b24-a218-598cacc0d70c · outbound

This paper cites Learning to extract semantic structure from documents using multimodal fully convolutional neural networks,.

HAND: Hierarchical Attention Network for Multi-Scale Handwritten Document Recognition and Layout Analysis Learning to extract semantic structure from documents using multimodal fully convolutional neural networks,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T01:04:47.477037Z

Source-reported events for the cited work

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Observation 776386ad-cb5e-4954-9cf0-82dd5c3a2461 · outbound

This paper cites dhsegment: A generic deep-learning approach for document segmentation,.

HAND: Hierarchical Attention Network for Multi-Scale Handwritten Document Recognition and Layout Analysis dhsegment: A generic deep-learning approach for document segmentation,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T01:04:47.466887Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 0ffcf130-ece2-4220-ada0-5d0cd0066e6a · outbound

This paper cites Multi-scale gated fully convolutional densenets for se- mantic labeling of historical newspaper images,.

HAND: Hierarchical Attention Network for Multi-Scale Handwritten Document Recognition and Layout Analysis Multi-scale gated fully convolutional densenets for se- mantic labeling of historical newspaper images,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T01:04:47.455787Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation b30bc671-57f5-4554-83a3-0d76043c4f6e · outbound

This paper cites Lay- outlm: Pre-training of text and layout for document image understanding,.

HAND: Hierarchical Attention Network for Multi-Scale Handwritten Document Recognition and Layout Analysis Lay- outlm: Pre-training of text and layout for document image understanding,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T01:04:47.444288Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 93f23a56-c3e1-48b0-a59b-9166df39f28b · outbound

This paper cites Visual attention for multi-task visual ques- tion answering,.

HAND: Hierarchical Attention Network for Multi-Scale Handwritten Document Recognition and Layout Analysis Visual attention for multi-task visual ques- tion answering,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T01:04:47.429773Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 75e202a2-5f9d-4399-a7c2-5b6b338b8516 · outbound

This paper cites A neural model for text localization, transcription and named entity recognition in full pages,.

HAND: Hierarchical Attention Network for Multi-Scale Handwritten Document Recognition and Layout Analysis A neural model for text localization, transcription and named entity recognition in full pages,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T01:04:47.418777Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 0a4bebb5-f9dd-4fdf-9068-7e59ed6cdab6 · outbound

This paper cites A computationally efficient pipeline approach to full page offline handwritten text recognition,.

HAND: Hierarchical Attention Network for Multi-Scale Handwritten Document Recognition and Layout Analysis A computationally efficient pipeline approach to full page offline handwritten text recognition,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T01:04:47.408183Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 7be85a77-41ea-4730-abe7-a7138d62bc13 · outbound

This paper cites A comprehensive studyofdocument imagelayoutanalysis,.

HAND: Hierarchical Attention Network for Multi-Scale Handwritten Document Recognition and Layout Analysis A comprehensive studyofdocument imagelayoutanalysis,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T01:04:47.398181Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 1d48335d-b9f4-41ec-8a9d-05cf800e6b7a · outbound

This paper cites A scalable handwritten text recognition system,.

HAND: Hierarchical Attention Network for Multi-Scale Handwritten Document Recognition and Layout Analysis A scalable handwritten text recognition system,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T01:04:47.388089Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 4c41e3f9-b487-4ee6-b5b8-8ec927ac4f35 · outbound

This paper cites Boosting handwriting text recognition in small databases with transfer learning,.

HAND: Hierarchical Attention Network for Multi-Scale Handwritten Document Recognition and Layout Analysis Boosting handwriting text recognition in small databases with transfer learning,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T01:04:47.377974Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 404af5eb-e07a-4b1d-87a1-77a7a2941b13 · outbound

This paper cites Intelligent character recognition using fully con- volutional neural networks,.

HAND: Hierarchical Attention Network for Multi-Scale Handwritten Document Recognition and Layout Analysis Intelligent character recognition using fully con- volutional neural networks,

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T01:04:47.366006Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation fd956d02-799d-4f9c-9ca1-ffa8b1444857 · outbound

This paper cites Deep residual learning for image recognition,.

HAND: Hierarchical Attention Network for Multi-Scale Handwritten Document Recognition and Layout Analysis Deep residual learning for image recognition,

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T01:04:47.353179Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation fd0bcdf7-fa23-4de6-906c-e6a6aa63128a · outbound

This paper cites Fully convolutional networks for semantic segmentation,.

HAND: Hierarchical Attention Network for Multi-Scale Handwritten Document Recognition and Layout Analysis Fully convolutional networks for semantic segmentation,

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T01:04:47.340894Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 258e4f32-d571-4615-8ef8-d31e0552d280 · outbound

This paper cites Xception: Deep learning with depthwise separable convolutions,.

HAND: Hierarchical Attention Network for Multi-Scale Handwritten Document Recognition and Layout Analysis Xception: Deep learning with depthwise separable convolutions,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T01:04:47.328550Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation fc70f054-f26f-4a92-8408-add93981c203 · outbound

This paper cites Drop an octave: Reducing spatial redundancy in convolutional neural networks with octave convolution,.

HAND: Hierarchical Attention Network for Multi-Scale Handwritten Document Recognition and Layout Analysis Drop an octave: Reducing spatial redundancy in convolutional neural networks with octave convolution,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T01:04:47.317273Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation e2b8369f-584d-4539-80a6-2e0362d90732 · outbound

This paper cites Squeeze-and-excitation networks,.

HAND: Hierarchical Attention Network for Multi-Scale Handwritten Document Recognition and Layout Analysis Squeeze-and-excitation networks,

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-11T01:04:46.774314Z

Source-reported events for the cited work

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Observation 3467e941-b60a-4546-9fa1-8c34f1f55491 · outbound

This paper cites Free- form image inpainting with gated convolution,.

HAND: Hierarchical Attention Network for Multi-Scale Handwritten Document Recognition and Layout Analysis Free- form image inpainting with gated convolution,

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T01:04:47.298069Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 3a2d6e61-2fdf-461a-b7b4-c812ca7f16bc · outbound

This paper cites Instance Normalization: The Missing Ingredient for Fast Stylization.

HAND: Hierarchical Attention Network for Multi-Scale Handwritten Document Recognition and Layout Analysis Instance Normalization: The Missing Ingredient for Fast Stylization

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-11T01:04:46.781619Z

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Observation fb501a41-0842-4631-b606-aab6ece1e64d · outbound

This paper cites Dropout: A simple way to prevent neural networks from overfitting,.

HAND: Hierarchical Attention Network for Multi-Scale Handwritten Document Recognition and Layout Analysis Dropout: A simple way to prevent neural networks from overfitting,

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T01:04:47.285190Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 223966e4-eb09-47ce-83cd-68648913736d · outbound

This paper cites Efficient object localization using convolutional networks,.

HAND: Hierarchical Attention Network for Multi-Scale Handwritten Document Recognition and Layout Analysis Efficient object localization using convolutional networks,

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T01:04:47.272903Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 8f3e1d6e-b8aa-4943-a286-7e700eb7d560 · outbound

This paper cites Attentionisallyouneed,.

HAND: Hierarchical Attention Network for Multi-Scale Handwritten Document Recognition and Layout Analysis Attentionisallyouneed,

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T01:04:47.261751Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 1324e009-2aad-4f7d-8de2-16d3da72d989 · outbound

This paper cites End- to-end memory networks,.

HAND: Hierarchical Attention Network for Multi-Scale Handwritten Document Recognition and Layout Analysis End- to-end memory networks,

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T01:04:47.249014Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 4a6f8557-e501-40d0-9430-aac8831e874e · outbound

This paper cites Scheduled sampling for sequence prediction with recurrent neural net- works,.

HAND: Hierarchical Attention Network for Multi-Scale Handwritten Document Recognition and Layout Analysis Scheduled sampling for sequence prediction with recurrent neural net- works,

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T01:04:47.237306Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation d7ece3eb-ab20-4c24-8956-e95789c63574 · outbound

This paper cites Curriculumdropout,.

HAND: Hierarchical Attention Network for Multi-Scale Handwritten Document Recognition and Layout Analysis Curriculumdropout,

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T01:04:47.227807Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 7c5bcfb5-5226-4409-86c3-f77c696242d1 · outbound

This paper cites AdaBatch: Adaptive Batch Sizes for Training Deep Neural Networks.

HAND: Hierarchical Attention Network for Multi-Scale Handwritten Document Recognition and Layout Analysis AdaBatch: Adaptive Batch Sizes for Training Deep Neural Networks

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-11T01:04:46.929368Z

Source-reported events for the cited work

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Observation 96d4a065-6d5e-48b1-a90f-ce5e762edab4 · outbound

This paper cites Browse Fonts - Google Fonts,.

HAND: Hierarchical Attention Network for Multi-Scale Handwritten Document Recognition and Layout Analysis Browse Fonts - Google Fonts,

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T01:04:47.217651Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation fd1d6f5b-4c95-414b-92ef-114d722eeaee · outbound

This paper cites an unresolved cited work.

HAND: Hierarchical Attention Network for Multi-Scale Handwritten Document Recognition and Layout Analysis Unresolved cited work

Reference 60

Resolution: unresolved; raw_fallback, observed 2026-08-11T01:04:47.207837Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation b205bfc7-ff13-4859-a699-d0f92d720589 · outbound

This paper cites Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing,.

HAND: Hierarchical Attention Network for Multi-Scale Handwritten Document Recognition and Layout Analysis Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing,

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T01:04:47.198545Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 890e16c2-d2c0-4485-a012-2b48a0c1195c · outbound

This paper cites Icfhr2016 competition on handwritten text recognition on the read dataset,.

HAND: Hierarchical Attention Network for Multi-Scale Handwritten Document Recognition and Layout Analysis Icfhr2016 competition on handwritten text recognition on the read dataset,

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T01:04:47.187300Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 256dff81-d162-41b1-a392-183c661954e7 · outbound

This paper cites Learning string-edit distance,.

HAND: Hierarchical Attention Network for Multi-Scale Handwritten Document Recognition and Layout Analysis Learning string-edit distance,

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T01:04:47.175906Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 2d2c185a-1147-44eb-9d10-e48c2a64f844 · outbound

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

HAND: Hierarchical Attention Network for Multi-Scale Handwritten Document Recognition and Layout Analysis End-to-end hand- written paragraph text recognition using a vertical attention network,

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T01:04:47.164724Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation c36a65f5-b387-472d-b369-0c9a0f9d700b · outbound

This paper cites an unresolved cited work.

HAND: Hierarchical Attention Network for Multi-Scale Handwritten Document Recognition and Layout Analysis Unresolved cited work

Reference 65

Resolution: unresolved; raw_fallback, observed 2026-08-11T01:04:47.152838Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 64dd8586-aefa-4de1-b2fd-eedcc19714c5 · outbound

This paper cites an unresolved cited work.

HAND: Hierarchical Attention Network for Multi-Scale Handwritten Document Recognition and Layout Analysis Unresolved cited work

Reference 66

Resolution: unresolved; raw_fallback, observed 2026-08-11T01:04:47.140145Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation ba76e934-def7-4802-ba9f-57aa60c972ea · outbound

This paper cites Our adaptation process leveraged the READ 2016 dataset, which provides consistent ground truth across all structural levels from line to triple-column.

HAND: Hierarchical Attention Network for Multi-Scale Handwritten Document Recognition and Layout Analysis Our adaptation process leveraged the READ 2016 dataset, which provides consistent ground truth across all structural levels from line to triple-column

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T01:04:47.127505Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 6a09b503-42ef-46f0-902e-47cc141b0db8 · outbound

This paper cites We started by utilizing SentencePiece tokenization, a technique rec- ognized for its prowess in handling subword units across diverse languages.

HAND: Hierarchical Attention Network for Multi-Scale Handwritten Document Recognition and Layout Analysis We started by utilizing SentencePiece tokenization, a technique rec- ognized for its prowess in handling subword units across diverse languages

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T01:04:47.115889Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation c4f93b86-5e0c-42be-9f64-34014e53704c · outbound

This paper cites We then cre- ated paired examples of (HADN prediction, ground truth) for each structural level, ranging from sentences to triple- column layouts.

HAND: Hierarchical Attention Network for Multi-Scale Handwritten Document Recognition and Layout Analysis We then cre- ated paired examples of (HADN prediction, ground truth) for each structural level, ranging from sentences to triple- column layouts

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T01:04:47.103724Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation ad34e85c-d572-40ec-894e-55ea2194cc56 · outbound

This paper cites Upon this robust foundation, we carefully integrated task-specific adapta- tion layers tailored to the demands of layout-aware error correction.

HAND: Hierarchical Attention Network for Multi-Scale Handwritten Document Recognition and Layout Analysis Upon this robust foundation, we carefully integrated task-specific adapta- tion layers tailored to the demands of layout-aware error correction

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T01:04:47.092523Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 6c4dfd46-8c9b-4c22-97f6-f2db59462232 · outbound

This paper cites an unresolved cited work.

HAND: Hierarchical Attention Network for Multi-Scale Handwritten Document Recognition and Layout Analysis Unresolved cited work

Reference 71

Resolution: unresolved; raw_fallback, observed 2026-08-11T01:04:47.080665Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation decb7a5c-88ea-4dfb-8cde-559b6ebd6159 · outbound

This paper cites HADN output processing involves extracting predicted text along with layout in- formation.

HAND: Hierarchical Attention Network for Multi-Scale Handwritten Document Recognition and Layout Analysis HADN output processing involves extracting predicted text along with layout in- formation

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T01:04:47.068678Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Reference resolution

Outbound references

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