https://github.com/user-attachments/assets/554cec6f-4883-42a6-b8e3-b64db05bc848
This system provides an end-to-end pipeline for processing manga pages to generate audio narration with character-specific voices. The pipeline consists of five main components: panel detection, text bubble detection with text extraction, character detection and clustering, speaker prediction through zero-shot iterative multimodal fusion, and text-to-audio generation using Higgs Audio V2.
The panel detection component receives raw manga page images as input. These images can be in standard formats including PNG, JPG, or JPEG. The images are processed as grayscale converted to RGB format to maintain consistency with the model's training data.
Panel detection uses the Magiv3 model, which is a multimodal encoder-decoder architecture. The model consists of a dual-attention Vision Transformer that serves as the image encoder. This encoder processes the visual information from the manga page and combines it with task-specific text prompts to guide the detection task.
The detection process begins with the ViT encoder analyzing the input image to create encoded visual representations. These encodings capture both local features of individual panels and global spatial relationships across the page. The model then combines these visual encodings with a panel detection prompt that instructs the model to identify panel boundaries.
The autoregressive decoder generates detection tokens sequentially. Each token represents quantized coordinates that define bounding box corners. The model has been trained to output these boxes in the correct manga reading order, which follows a right-to-left, top-to-bottom pattern for Japanese manga. This ordering is crucial because the sequence of panels determines the narrative flow of the dialogue.
The quantization of coordinates allows the model to treat object detection as a token generation task, similar to language modeling. This unified approach enables the same architecture to handle multiple vision tasks through different prompts.
The panel detection component outputs an ordered list of bounding boxes. Each bounding box is represented by four coordinates (x1, y1, x2, y2) that define the top-left and bottom-right corners of the detected panel. The boxes are already sorted in the intended reading order, eliminating the need for post-processing to determine panel sequence.
This component takes the same manga page images as input, along with the panel detection results from the previous stage. The images are processed in their entirety rather than being cropped to individual panels, which allows the model to maintain context about text bubble relationships across panel boundaries.
Text detection employs the same Magiv3 multimodal encoder-decoder model but with different task-specific prompts. The architecture remains consistent: a dual-attention ViT encoder paired with an autoregressive decoder. This reuse of the same model for multiple tasks demonstrates the versatility of the prompt-based approach.
The text detection and extraction process involves two distinct stages, each requiring different prompts to the Magiv3 model.
In the first stage, the model receives a text detection prompt that instructs it to identify all text-containing regions in the manga page. This includes speech bubbles, thought bubbles, narrative boxes, and sound effects. The model outputs bounding boxes for each detected text region, similar to the panel detection process. These boxes encompass the entire bubble or text area, not just the text itself.
The second stage focuses on text extraction through OCR. The model receives an additional prompt along with the detected text regions from the first stage. For each text box, the model generates tokens that represent the actual text content. This OCR capability is built into the model's decoder, which has been trained on paired image-text data to learn the mapping between visual text appearance and character sequences.
A critical challenge in this stage is matching the extracted text back to its corresponding detection box. The model maintains alignment between text regions and extracted content through positional encoding and attention mechanisms. The system ensures that each piece of extracted text is correctly associated with its source bubble or text box, preserving the spatial relationship between text and its location on the page.
The component produces a list of text box coordinates paired with their extracted text content. Each entry contains the bounding box coordinates (x1, y1, x2, y2) and the corresponding text string extracted through OCR. This paired output maintains the spatial-textual relationship necessary for subsequent processing stages.
The character detection and clustering component receives character bounding boxes that were detected during the initial detection phase. These boxes represent individual character appearances across all panels in the manga pages. Each detection includes the visual region containing a character but lacks information about character identity.
Character clustering employs an association MLP that computes pairwise similarity scores between character detections. This MLP has been trained to recognize visual similarities between different appearances of the same character, accounting for variations in pose, expression, and viewing angle.
The clustering process treats the character detection problem as a graph construction and analysis task. Each detected character box becomes a node in an undirected graph. The association MLP processes pairs of character detections to compute similarity scores, which represent the likelihood that two detections show the same character.
For each pair of character detections, the MLP examines visual features extracted from the character regions. These features include facial characteristics, clothing patterns, hair style, and other distinguishing visual attributes. The MLP outputs a confidence score between 0 and 1, where higher scores indicate greater similarity.
The similarity scores form weighted edges in the character graph. A threshold is applied to these scores to determine which edges to include in the final graph structure. Edges with scores below the threshold are discarded to reduce noise and prevent incorrect associations.
The algorithm then identifies connected components within this graph using standard graph traversal techniques. Each connected component represents a cluster of character detections that are believed to show the same individual. The clustering algorithm does not use explicit edge tokens but relies on the pairwise scores to establish connections.
The clustering must handle several challenges. Characters may appear in different poses, expressions, or partial occlusions across panels. The same character might wear different clothes in different scenes. The MLP has been trained to be robust to these variations while still distinguishing between different characters with similar appearances.
The clustering process produces a persistent character roster where each unique character is assigned a cluster identifier. Every character detection is labeled with its cluster ID, creating consistency across all panels. This roster maintains character identity throughout the manga sequence, enabling consistent speaker attribution in later stages.
The speaker prediction component receives multiple inputs: extracted dialogues from text boxes, character detections with their cluster assignments, and the relationships between text and character regions. This multimodal input provides both textual and visual information for speaker attribution.
Speaker prediction employs a combination of LLMs for text analysis and lightweight image classifiers for visual character identification. The system uses GPT-4 or similar models for context-aware text analysis, while ResNet-based classifiers handle visual character classification tasks.
The speaker prediction process implements an iterative refinement strategy that progressively improves accuracy through multiple cycles of multimodal fusion.
Initial Text-Only Prediction: The process begins with the LLM analyzing all extracted dialogues to identify character names mentioned in the text. The model performs context-aware name extraction, understanding that names might appear in various forms including full names, nicknames, or titles. The LLM generates a story summary to understand the narrative context and creates character profiles based on textual evidence. This helps establish relationships between characters and their likely speaking patterns.
Due to token limitations in LLMs, long manga chapters are processed in chunks. Each chunk maintains sufficient context overlap to preserve narrative coherence. The LLM processes each chunk and outputs predictions in a structured format containing four elements: TextID (identifying the specific dialogue), CharacterName (the predicted speaker's name), CharacterID (a unique identifier for the character), and Confidence (a score from 1 to 5 indicating prediction certainty).
Iterative Refinement: After initial predictions, the system enters an iterative refinement phase that typically runs for three to four cycles.
In each iteration, label propagation occurs bidirectionally between text and character regions. Text-to-character propagation takes high-confidence speaker predictions from text analysis and uses them as pseudo-labels for training character classifiers. Only predictions with confidence scores of 3 or higher are used for pseudo-labeling to maintain quality.
Character classification involves training lightweight ResNet classifiers on the pseudo-labeled data. These classifiers learn to recognize visual patterns associated with each character identity. The classifiers use data augmentation techniques including horizontal flipping and brightness adjustments to improve robustness. Multiple ensemble models are trained with different random seeds to reduce overfitting.
Character-to-text propagation then occurs, where visual character classifications inform text speaker predictions. For each text region, the system identifies nearby character detections based on spatial relationships. The visual classifications provide candidate speakers for each dialogue, creating a shortlist that the LLM can evaluate in the next iteration.
Relationship re-scoring dynamically adjusts the association strengths between text and character regions. When textual and visual predictions agree on a speaker assignment, the relationship score increases. When they disagree, the score decreases. These adjusted scores influence subsequent iterations, gradually reinforcing correct associations.
The LLM performs refined predictions using both the original text and the visual candidate suggestions. The model evaluates whether the suggested speakers from visual analysis align with linguistic cues in the dialogue. This multimodal information leads to more accurate and confident predictions.
Confidence scoring plays a crucial role throughout the process. The system maintains confidence scores for both textual and visual predictions. Low-confidence predictions are filtered out to prevent error propagation. The confidence threshold typically sits at 3 out of 5, though this can be adjusted based on the desired precision-recall trade-off.
The speaker prediction component outputs speaker assignments for each text region, including the character name or ID, confidence scores ranging from 1 to 5, and character cluster labels that maintain consistency across panels. The output also includes the refined relationships between text and character regions with updated association scores.
The audio generation component receives the extracted text with speaker assignments from the previous stage. Each text segment includes the dialogue content, the identified speaker name or ID, and the confidence score for the speaker assignment. The component also receives any reference audio samples if voice cloning is desired.
Higgs Audio V2 consists of a 3.6B parameter Large Language Model combined with a 2.2B parameter audio adapter. The system uses a unified audio tokenizer that captures both semantic and acoustic features. The architecture employs dual feed-forward networks to enhance the model's ability to generate acoustic tokens with minimal computational overhead.
The audio generation process transforms text and speaker information into naturalistic speech with character-appropriate voices.
Voice Registry Creation: The system first establishes a voice registry that maps each character to a specific voice profile. For characters with high-confidence speaker assignments (confidence >= 3), the system generates or assigns a unique voice. Each character receives custom reference audio that defines their vocal characteristics including pitch, tone, speaking rate, and accent.
For characters with low-confidence assignments or when confidence falls below the threshold, the system uses a default narrator voice. This ensures that all text receives audio generation while maintaining distinction between confident and uncertain attributions.
Audio Generation Process: The Higgs Audio V2 model processes text segments sequentially, maintaining consistency within each speaker's voice across all their dialogues. The model receives the text content along with scene description prompts that provide context about the audio environment.
The system uses single-speaker voice generation mode when processing individual character dialogues. This mode ensures voice consistency by referencing the voice registry for each character. The model maintains the same voice characteristics across all utterances from a single character, regardless of their position in the manga.
The generation process incorporates prosody cues from punctuation and textual analysis. Question marks trigger rising intonation patterns. Exclamation marks increase energy and volume. Ellipses create pauses and trailing effects. The model adapts these prosodic features while maintaining the character's base voice profile.
The DualFFN architecture enables the model to generate both semantic tokens (representing meaning) and acoustic tokens (representing sound characteristics) efficiently. This dual processing allows for high-quality audio that maintains both intelligibility and naturalness.
For multi-speaker scenes where characters interact, the system can generate smooth transitions between different character voices. The model has been trained on over 10 million hours of audio data, enabling it to produce natural-sounding conversations with appropriate turn-taking and emotional expression.
Temperature and sampling parameters control the generation variability. Lower temperature values (around 0.3) produce more consistent and predictable speech, while higher values introduce more variation and expressiveness. The system typically uses lower temperatures for narrative text and slightly higher values for emotional dialogue.
The audio generation component produces audio files in WAV format with a sampling rate of 24kHz or higher. Each audio segment corresponds to a text region with the appropriate character voice applied. The output includes synchronized timing information that aligns audio segments with their corresponding visual panels and text boxes. The system also generates a composite audio track that combines all individual segments in reading order.
The complete manga reader pipeline processes manga pages through five integrated stages:
Panel Detection: Raw manga images enter the Magiv3 model, which identifies and orders all panels according to manga reading conventions. This establishes the narrative sequence for all subsequent processing.
Text Detection and Extraction: The same images are processed to detect text bubbles and extract their content through OCR. Each text region is matched with its extracted content and optionally associated with its containing panel.
Character Detection and Clustering: Character appearances are detected across all panels and clustered into consistent identities using visual similarity scoring. This creates a persistent character roster that maintains identity across the entire manga.
Speaker Prediction: The system performs initial text-based speaker prediction using LLMs, then iteratively refines these predictions through multimodal fusion. Visual character information and textual dialogue content work together through multiple refinement cycles to achieve accurate speaker attribution.
Audio Generation: Finally, Higgs Audio V2 converts the attributed text into character-voiced audio, using voice profiles mapped to each character identity.
Information flows through the pipeline with increasing enrichment at each stage:
Each component's output becomes input for subsequent stages, creating a cascading enrichment process. The panel ordering informs text sequence, character clustering enables consistent speaker attribution, and speaker assignments guide voice selection in audio generation.
The complete pipeline produces multiple outputs:
Structured Data: JSON files containing all detection results including panel bounding boxes with reading order, text box coordinates with extracted content, character cluster assignments and roster, speaker-text associations with confidence scores, and relationship scores between text and character regions.
Human-Readable Transcript: A formatted text file with speaker-attributed dialogues in proper reading order. Each line includes the speaker name and their dialogue, formatted for easy reading and further processing.
Audio Narration: WAV audio files with character-specific voices for each dialogue. A complete audio track combining all segments in reading order. Timing information for synchronizing audio with visual content.
Visualization: Annotated manga pages showing detected panels, text boxes, and character regions. Relationship visualizations showing text-to-character associations. Color-coded overlays indicating detection confidence levels.
https://github.com/user-attachments/assets/554cec6f-4883-42a6-b8e3-b64db05bc848
This system provides an end-to-end pipeline for processing manga pages to generate audio narration with character-specific voices. The pipeline consists of five main components: panel detection, text bubble detection with text extraction, character detection and clustering, speaker prediction through zero-shot iterative multimodal fusion, and text-to-audio generation using Higgs Audio V2.
The panel detection component receives raw manga page images as input. These images can be in standard formats including PNG, JPG, or JPEG. The images are processed as grayscale converted to RGB format to maintain consistency with the model's training data.
Panel detection uses the Magiv3 model, which is a multimodal encoder-decoder architecture. The model consists of a dual-attention Vision Transformer that serves as the image encoder. This encoder processes the visual information from the manga page and combines it with task-specific text prompts to guide the detection task.
The detection process begins with the ViT encoder analyzing the input image to create encoded visual representations. These encodings capture both local features of individual panels and global spatial relationships across the page. The model then combines these visual encodings with a panel detection prompt that instructs the model to identify panel boundaries.
The autoregressive decoder generates detection tokens sequentially. Each token represents quantized coordinates that define bounding box corners. The model has been trained to output these boxes in the correct manga reading order, which follows a right-to-left, top-to-bottom pattern for Japanese manga. This ordering is crucial because the sequence of panels determines the narrative flow of the dialogue.
The quantization of coordinates allows the model to treat object detection as a token generation task, similar to language modeling. This unified approach enables the same architecture to handle multiple vision tasks through different prompts.
The panel detection component outputs an ordered list of bounding boxes. Each bounding box is represented by four coordinates (x1, y1, x2, y2) that define the top-left and bottom-right corners of the detected panel. The boxes are already sorted in the intended reading order, eliminating the need for post-processing to determine panel sequence.
This component takes the same manga page images as input, along with the panel detection results from the previous stage. The images are processed in their entirety rather than being cropped to individual panels, which allows the model to maintain context about text bubble relationships across panel boundaries.
Text detection employs the same Magiv3 multimodal encoder-decoder model but with different task-specific prompts. The architecture remains consistent: a dual-attention ViT encoder paired with an autoregressive decoder. This reuse of the same model for multiple tasks demonstrates the versatility of the prompt-based approach.
The text detection and extraction process involves two distinct stages, each requiring different prompts to the Magiv3 model.
In the first stage, the model receives a text detection prompt that instructs it to identify all text-containing regions in the manga page. This includes speech bubbles, thought bubbles, narrative boxes, and sound effects. The model outputs bounding boxes for each detected text region, similar to the panel detection process. These boxes encompass the entire bubble or text area, not just the text itself.
The second stage focuses on text extraction through OCR. The model receives an additional prompt along with the detected text regions from the first stage. For each text box, the model generates tokens that represent the actual text content. This OCR capability is built into the model's decoder, which has been trained on paired image-text data to learn the mapping between visual text appearance and character sequences.
A critical challenge in this stage is matching the extracted text back to its corresponding detection box. The model maintains alignment between text regions and extracted content through positional encoding and attention mechanisms. The system ensures that each piece of extracted text is correctly associated with its source bubble or text box, preserving the spatial relationship between text and its location on the page.
The component produces a list of text box coordinates paired with their extracted text content. Each entry contains the bounding box coordinates (x1, y1, x2, y2) and the corresponding text string extracted through OCR. This paired output maintains the spatial-textual relationship necessary for subsequent processing stages.
The character detection and clustering component receives character bounding boxes that were detected during the initial detection phase. These boxes represent individual character appearances across all panels in the manga pages. Each detection includes the visual region containing a character but lacks information about character identity.
Character clustering employs an association MLP that computes pairwise similarity scores between character detections. This MLP has been trained to recognize visual similarities between different appearances of the same character, accounting for variations in pose, expression, and viewing angle.
The clustering process treats the character detection problem as a graph construction and analysis task. Each detected character box becomes a node in an undirected graph. The association MLP processes pairs of character detections to compute similarity scores, which represent the likelihood that two detections show the same character.
For each pair of character detections, the MLP examines visual features extracted from the character regions. These features include facial characteristics, clothing patterns, hair style, and other distinguishing visual attributes. The MLP outputs a confidence score between 0 and 1, where higher scores indicate greater similarity.
The similarity scores form weighted edges in the character graph. A threshold is applied to these scores to determine which edges to include in the final graph structure. Edges with scores below the threshold are discarded to reduce noise and prevent incorrect associations.
The algorithm then identifies connected components within this graph using standard graph traversal techniques. Each connected component represents a cluster of character detections that are believed to show the same individual. The clustering algorithm does not use explicit edge tokens but relies on the pairwise scores to establish connections.
The clustering must handle several challenges. Characters may appear in different poses, expressions, or partial occlusions across panels. The same character might wear different clothes in different scenes. The MLP has been trained to be robust to these variations while still distinguishing between different characters with similar appearances.
The clustering process produces a persistent character roster where each unique character is assigned a cluster identifier. Every character detection is labeled with its cluster ID, creating consistency across all panels. This roster maintains character identity throughout the manga sequence, enabling consistent speaker attribution in later stages.
The speaker prediction component receives multiple inputs: extracted dialogues from text boxes, character detections with their cluster assignments, and the relationships between text and character regions. This multimodal input provides both textual and visual information for speaker attribution.
Speaker prediction employs a combination of LLMs for text analysis and lightweight image classifiers for visual character identification. The system uses GPT-4 or similar models for context-aware text analysis, while ResNet-based classifiers handle visual character classification tasks.
The speaker prediction process implements an iterative refinement strategy that progressively improves accuracy through multiple cycles of multimodal fusion.
Initial Text-Only Prediction: The process begins with the LLM analyzing all extracted dialogues to identify character names mentioned in the text. The model performs context-aware name extraction, understanding that names might appear in various forms including full names, nicknames, or titles. The LLM generates a story summary to understand the narrative context and creates character profiles based on textual evidence. This helps establish relationships between characters and their likely speaking patterns.
Due to token limitations in LLMs, long manga chapters are processed in chunks. Each chunk maintains sufficient context overlap to preserve narrative coherence. The LLM processes each chunk and outputs predictions in a structured format containing four elements: TextID (identifying the specific dialogue), CharacterName (the predicted speaker's name), CharacterID (a unique identifier for the character), and Confidence (a score from 1 to 5 indicating prediction certainty).
Iterative Refinement: After initial predictions, the system enters an iterative refinement phase that typically runs for three to four cycles.
In each iteration, label propagation occurs bidirectionally between text and character regions. Text-to-character propagation takes high-confidence speaker predictions from text analysis and uses them as pseudo-labels for training character classifiers. Only predictions with confidence scores of 3 or higher are used for pseudo-labeling to maintain quality.
Character classification involves training lightweight ResNet classifiers on the pseudo-labeled data. These classifiers learn to recognize visual patterns associated with each character identity. The classifiers use data augmentation techniques including horizontal flipping and brightness adjustments to improve robustness. Multiple ensemble models are trained with different random seeds to reduce overfitting.
Character-to-text propagation then occurs, where visual character classifications inform text speaker predictions. For each text region, the system identifies nearby character detections based on spatial relationships. The visual classifications provide candidate speakers for each dialogue, creating a shortlist that the LLM can evaluate in the next iteration.
Relationship re-scoring dynamically adjusts the association strengths between text and character regions. When textual and visual predictions agree on a speaker assignment, the relationship score increases. When they disagree, the score decreases. These adjusted scores influence subsequent iterations, gradually reinforcing correct associations.
The LLM performs refined predictions using both the original text and the visual candidate suggestions. The model evaluates whether the suggested speakers from visual analysis align with linguistic cues in the dialogue. This multimodal information leads to more accurate and confident predictions.
Confidence scoring plays a crucial role throughout the process. The system maintains confidence scores for both textual and visual predictions. Low-confidence predictions are filtered out to prevent error propagation. The confidence threshold typically sits at 3 out of 5, though this can be adjusted based on the desired precision-recall trade-off.
The speaker prediction component outputs speaker assignments for each text region, including the character name or ID, confidence scores ranging from 1 to 5, and character cluster labels that maintain consistency across panels. The output also includes the refined relationships between text and character regions with updated association scores.
The audio generation component receives the extracted text with speaker assignments from the previous stage. Each text segment includes the dialogue content, the identified speaker name or ID, and the confidence score for the speaker assignment. The component also receives any reference audio samples if voice cloning is desired.
Higgs Audio V2 consists of a 3.6B parameter Large Language Model combined with a 2.2B parameter audio adapter. The system uses a unified audio tokenizer that captures both semantic and acoustic features. The architecture employs dual feed-forward networks to enhance the model's ability to generate acoustic tokens with minimal computational overhead.
The audio generation process transforms text and speaker information into naturalistic speech with character-appropriate voices.
Voice Registry Creation: The system first establishes a voice registry that maps each character to a specific voice profile. For characters with high-confidence speaker assignments (confidence >= 3), the system generates or assigns a unique voice. Each character receives custom reference audio that defines their vocal characteristics including pitch, tone, speaking rate, and accent.
For characters with low-confidence assignments or when confidence falls below the threshold, the system uses a default narrator voice. This ensures that all text receives audio generation while maintaining distinction between confident and uncertain attributions.
Audio Generation Process: The Higgs Audio V2 model processes text segments sequentially, maintaining consistency within each speaker's voice across all their dialogues. The model receives the text content along with scene description prompts that provide context about the audio environment.
The system uses single-speaker voice generation mode when processing individual character dialogues. This mode ensures voice consistency by referencing the voice registry for each character. The model maintains the same voice characteristics across all utterances from a single character, regardless of their position in the manga.
The generation process incorporates prosody cues from punctuation and textual analysis. Question marks trigger rising intonation patterns. Exclamation marks increase energy and volume. Ellipses create pauses and trailing effects. The model adapts these prosodic features while maintaining the character's base voice profile.
The DualFFN architecture enables the model to generate both semantic tokens (representing meaning) and acoustic tokens (representing sound characteristics) efficiently. This dual processing allows for high-quality audio that maintains both intelligibility and naturalness.
For multi-speaker scenes where characters interact, the system can generate smooth transitions between different character voices. The model has been trained on over 10 million hours of audio data, enabling it to produce natural-sounding conversations with appropriate turn-taking and emotional expression.
Temperature and sampling parameters control the generation variability. Lower temperature values (around 0.3) produce more consistent and predictable speech, while higher values introduce more variation and expressiveness. The system typically uses lower temperatures for narrative text and slightly higher values for emotional dialogue.
The audio generation component produces audio files in WAV format with a sampling rate of 24kHz or higher. Each audio segment corresponds to a text region with the appropriate character voice applied. The output includes synchronized timing information that aligns audio segments with their corresponding visual panels and text boxes. The system also generates a composite audio track that combines all individual segments in reading order.
The complete manga reader pipeline processes manga pages through five integrated stages:
Panel Detection: Raw manga images enter the Magiv3 model, which identifies and orders all panels according to manga reading conventions. This establishes the narrative sequence for all subsequent processing.
Text Detection and Extraction: The same images are processed to detect text bubbles and extract their content through OCR. Each text region is matched with its extracted content and optionally associated with its containing panel.
Character Detection and Clustering: Character appearances are detected across all panels and clustered into consistent identities using visual similarity scoring. This creates a persistent character roster that maintains identity across the entire manga.
Speaker Prediction: The system performs initial text-based speaker prediction using LLMs, then iteratively refines these predictions through multimodal fusion. Visual character information and textual dialogue content work together through multiple refinement cycles to achieve accurate speaker attribution.
Audio Generation: Finally, Higgs Audio V2 converts the attributed text into character-voiced audio, using voice profiles mapped to each character identity.
Information flows through the pipeline with increasing enrichment at each stage:
Each component's output becomes input for subsequent stages, creating a cascading enrichment process. The panel ordering informs text sequence, character clustering enables consistent speaker attribution, and speaker assignments guide voice selection in audio generation.
The complete pipeline produces multiple outputs:
Structured Data: JSON files containing all detection results including panel bounding boxes with reading order, text box coordinates with extracted content, character cluster assignments and roster, speaker-text associations with confidence scores, and relationship scores between text and character regions.
Human-Readable Transcript: A formatted text file with speaker-attributed dialogues in proper reading order. Each line includes the speaker name and their dialogue, formatted for easy reading and further processing.
Audio Narration: WAV audio files with character-specific voices for each dialogue. A complete audio track combining all segments in reading order. Timing information for synchronizing audio with visual content.
Visualization: Annotated manga pages showing detected panels, text boxes, and character regions. Relationship visualizations showing text-to-character associations. Color-coded overlays indicating detection confidence levels.
