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# PALM-LAY: A Multi-Script Cross-Regional Dataset for Layout Analysis of Palm Leaf Manuscripts

Nimol Thuon, Jun Du, Panhapin Theang, Ratana Thuon

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## To cite this version:

Nimol Thuon, Jun Du, Panhapin Theang, Ratana Thuon. PALM-LAY: A Multi-Script Cross-Regional Dataset for Layout Analysis of Palm Leaf Manuscripts. International Conference on Document Analysis and Recognition Workshops, ICDAR 2025, Sep 2025, Wuhan, China. hal-05220442

HAL Id: hal-05220442

https://hal.science/hal-05220442v1

Submitted on 23 Aug 2025

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Distributed under a Creative Commons Attribution 4.0 International License
PALM-LAY: A Multi-Script Cross-Regional
Dataset for Layout Analysis of Palm Leaf
Manuscripts

Nimol Thuon¹, Jun Du¹*, Panhapin Theang², and Ratana Thuon³

¹ National Engineering Research Center of Speech and Language Information Processing, University of Science and Technology of China, 230026, Hefei, China tnimol@mail.ustc.edu.cn, jundu@ustc.edu.cn

² School of Public Affairs, University of Science and Technology of China, 230026, Hefei, China

³ School of Management, Shanghai Lixin University of Accounting and Finance, 201620, Shanghai, China

**Abstract.** Among the oldest forms of written heritage in Asia, palm leaf manuscripts preserve centuries of cultural, religious, literary, and scientific knowledge. However, their analysis using modern document imaging techniques remains limited due to challenges such as script diversity, complex layouts, and physical degradation. While regional research efforts are growing, publicly available annotated datasets remain scarce and fragmented. Notably, many of these ancient, low-resource scripts exhibit unique linguistic characteristics while sharing common structural layout features. In this paper, we introduce PALM-LAY, the first unified, cross-regional annotated dataset specifically designed for the layout analysis of historical palm leaf manuscripts. The dataset includes over 566 pages and more than 6,000 annotated regions, covering six distinct scripts: Tamil, Kambaramayanam, Jathakam, Khmer, Sun-danese, and Balinese. These samples were carefully curated from multiple publicly available collections and annotated using a unified layout schema. Each image is annotated with seven region categories: MainRegion, TextLineRegion, ParagraphRegion, SymbolicMark, PhysicalDam age, Illustration, and Other, capturing both traditional manuscript structures and modern archival additions. We benchmark several state-of-the-art object detection models to evaluate layout analysis performance across scripts. The results highlight the challenges of cross-script generalization and underscore the need for culturally informed, layout-aware models. PALM-LAY is intended to advance research in multilingual document understanding and support the digital preservation of endangered manuscript traditions. The dataset and documentation will be publicly released at: https://github.com/back-kh/PALM-LAY.

**Keywords:** Palm leaf manuscripts · Historical document analysis · Layout analysis · Document annotation

<footnote>* corresponding author</footnote>
# 1 Introduction

Palm leaf manuscripts represent one of Asia's oldest surviving forms of written heritage, predominantly found across South and Southeast Asian regions [8,10,16]. Inscribed with styluses on carefully prepared dried palm leaves and preserved with natural dyes, these invaluable artifacts encapsulate centuries of cultural, religious, literary, and scientific knowledge. Notable examples include sacred Hindu and Buddhist scriptures, classical Tamil epics, intricate astrological charts, and indigenous medical treatises. Many of these works, some dating back over a thousand years, remain indispensable primary sources for historians, linguists, and cultural heritage scholars [23,31,34,35]. Despite their profound historical significance, palm leaf manuscripts are vastly underexplored in modern document image analysis. Progress in this domain is significantly hindered by several unique challenges: immense script diversity, the inherent complexity of their visual layout structures, varying degrees of physical degradation over time, and the low-resource nature of many involved languages [20,5]. Manuscripts from distinct regions, such as Tamil and Malayalam from India, or Khmer, Balinese, and Sundanese from Southeast Asia, often exhibit unique spatial arrangements, distinctive decorative elements, and script-specific conventions [8,9,24,29,16]. These variations collectively complicate the development of robust and generalizable computational models.

<figure><img src="image_4.png" /><figcaption>**Fig. 1.** Examples of historical palm leaf manuscript images from different scripts and regions: (a) Kambaramayanam, (b) Jathakam, (c) Balinese, (d) Tamil, (e) Khmer, and (f) Sundanese. These samples illustrate the diversity in script styles, document conditions, and physical formats across South and Southeast Asia.</figcaption></figure>

Layout analysis serves as a fundamental and crucial prerequisite for understanding such complex historical documents [4,3,13]. It effectively bridges the gap between raw image processing and subsequent downstream tasks like Optical Character Recognition (OCR), keyword spotting, or sophisticated content classification [33,12,26]. Unlike standard document datasets that typically assume regular, printed layouts, palm leaf manuscripts present highly variable and
inherently noisy layouts: irregular text lines, physically damaged margins, intri-
cate symbolic marks, and diverse illustrations embedded within or around the
textual content. Furthermore, many manuscripts frequently incorporate modern
archival elements such as stickers, stamps, or handwritten annotations super-
imposed during preservation efforts, which further complicate automated layout
understanding [10,8,28,27]. Although several digitization initiatives have made
commendable strides in preserving palm leaf manuscripts, existing publicly an-
notated datasets remain highly fragmented, uneven in quality, and often con-
fined to specific regions or scripts. As depicted in Fig. 1, sample collections from
South and Southeast Asia, including Tamil, Malayalam, Balinese, Sundanese,
and Khmer scripts, demonstrate this diversity. While some collections offer rela-
tively clean manuscripts suitable for document analysis [10], others, particularly
those involving Tamil and Malayalam scripts, are still focused on early-stage bi-
narization or basic image enhancement [16,8]. Crucially, very few offer compre-
hensive, multi-class layout annotations or are structured to support multi-script
benchmarking. Moreover, the accurate annotation of palm leaf manuscripts is a
labor-intensive task demanding specialized domain knowledge, especially given
the historical, damaged, and low-resource nature of the texts.

To systematically address these significant limitations, we introduce PALM-LAY, the first unified, cross-regional, multi-script dataset specifically designed for comprehensive layout analysis in historical palm leaf manuscripts. PALM-LAY comprises 566 meticulously annotated manuscript images, strategically sourced from multiple publicly available collections and rigorously annotated under a standardized, consistent layout schema. The dataset spans six distinct scripts: Tamil, Kambaramayanam, Jathakam, Khmer, Sundanese, and Balinese, and provides precise annotations for seven layout categories: *MainRegion*, *ParagraphRegion*, *TextLineRegion*, *SymbolicMark*, *PhysicalDamage*, *Illustration*, and *Other*. These classes have been thoughtfully designed to capture the diverse visual and structural elements ubiquitously found across various manuscript traditions, encompassing both native features and non-native archival additions. In this paper, we comprehensively describe the construction methodology of the PALM-LAY dataset, provide a detailed analysis of layout characteristics observed across its constituent scripts, and establish robust benchmarks for layout detection performance using a range of state-of-the-art object detection models.

In this paper, our contributions are as follows:

1. We introduce PALM-LAY, the first unified, cross-regional, multi-script annotated dataset explicitly designed for layout analysis of historical palm leaf manuscripts, by combining and meticulously annotating over 566 pages and over 6,000 annotations across six distinct scripts.

2. We provide a detailed analysis of layout characteristics observed across these 6 scripts, highlighting the structural diversity and unique challenges specific to each manuscript.

3. We establish comprehensive benchmarks for layout analysis by evaluating a diverse set of recent object detection models on the PALM-LAY dataset, rigorously assessing their cross-script generalization capabilities.
# 2 Related Work

## 2.1 Historical Document Layout Analysis

Layout analysis is a fundamental task in document image understanding, particularly for historical collections that exhibit irregular structures, handwritten content, and various forms of degradation [4,3]. Over the years, several competitions most notably those organized as part of the ICDAR conference series have significantly advanced the state of the art in historical document layout analysis. These include the ICDAR 2017 [21] and 2019 competitions on Historical Book Analysis (HBA2019) [15], and the Extended Layout Analysis (ICDAR 2021) challenge [13], which focused on multi-class region segmentation in complex, noisy historical documents.

The PRImA Layout Analysis dataset [1] has been widely adopted as a benchmark for layout segmentation tasks, offering real-world scanned images of printed historical newspapers. However, such datasets are primarily Western and Latin-script centric, characterized by well-defined structural norms and limited cultural variability. In contrast, palm leaf manuscripts represent non-standard formats with handwritten text in diverse scripts, intricate decorative elements, and archival overlays, posing unique challenges beyond those addressed by existing benchmarks.

## 2.2 Multilingual Layout Detection and Cross-Script Models

Recent advances in document understanding have led to the development of multilingual and layout-aware models capable of processing both textual and visual features. Models such as LayoutLM [33], LayoutLMv2 [32], and LayoutLMv3 [7] seamlessly integrate textual, spatial, and visual modalities for document image understanding. Similarly, Donut [12], a transformer-based model that eliminates the need for traditional OCR, has demonstrated impressive zero-shot capabilities across various document types. In parallel, detection-based models have proven highly effective for identifying layout components like text blocks, figures, and tables. These include the YOLO series [17,30,11], Faster R-CNN [18], and transformer-based approaches such as DETR [2] and its recent variants like RF-DETR [19]. These models are well-suited for layout region segmentation in complex documents and serve as strong baselines for layout analysis tasks.

While LayoutLM variants [33,32,7] and Donut [12] are powerful tools for document understanding, they rely on OCR-generated text tokens and aligned text-image pairs. However, PALM-LAY is exclusively annotated with layout regions and does not include text transcriptions or token-level information. This makes multimodal approaches inapplicable for our current dataset. Moreover, these models are predominantly trained on Latin-script, high-resource datasets, limiting their effectiveness on ancient, non-Latin scripts with irregular layouts and degraded conditions. Therefore, we focus on vision-based object detectors that are better suited for region-level layout segmentation in complex historical manuscripts.
3 Palm Leaf Manuscript Collections and Characteristics

Palm leaf manuscripts have served as a vital and enduring medium for preserv-
ing knowledge across South and Southeast Asia for centuries. These invaluable
artifacts were extensively utilized in diverse regions such as Tamil Nadu and
Kerala in India, Cambodia, Bali, and Java in Indonesia [10,25,8,16].

The preparation of palm leaves for inscription involved meticulous processes of drying, polishing, and sometimes smoking, followed by the actual inscription using sharp styluses. Texts were subsequently preserved by applying organic dyes that darkened the stylus grooves. The content was typically arranged on long, narrow leaves, traditionally bound through central holes and protected by wooden covers. Although the overarching physical formats remained largely consistent, the visual and linguistic characteristics of the manuscripts varied significantly across different scripts and regions, reflecting unique cultural aesthetics and writing conventions [22,14].

This study strategically incorporates manuscript images from multiple pub-
licly available datasets, each released under open licenses such as Creative Com-
mons (e.g., CC BY-NC-SA 3.0⁴, CC BY-NC-SA 4.0⁵, and CC-BY 4.0⁶). This
ensures broad accessibility and reusability of the data. These curated collections
originate from various heritage digitization efforts spanning India, Cambodia,
and Indonesia. Specifically, our dataset includes:

1. **Tamil** [8]: This collection primarily features manuscripts from the TH-PLMD dataset, containing classical Tamil texts such as *Naladiyar*, *Tholkappiyam*, and *Thirikadugam*, representing a rich literary heritage from India.

2. **Jathakam** [16]: Sourced from India, this collection specifically includes astrological horoscopes, offering a unique content focus within the Malayalam script tradition.

3. **Kambaramayanam** [16]: Also from India, this set comprises manuscripts of the epic literary work *Kambaramayanam*, highlighting another significant aspect of textual heritage.

4. **Khmer** [29,10]: These manuscripts originate from various Cambodian archive collections, providing examples of historical documents with distinctive South-east Asian script characteristics and layout styles.

5. **Sundanese** [24,10]: The Sundanese samples are carefully selected from regional repositories in Indonesia, showcasing the unique visual features and cultural context of Sundanese palm leaf writing.

6. **Balinese** [9,10]: Primarily obtained from the Bali Manuscript Collection, these manuscripts are known for their intricate script, often ornate decorative elements, and specific binding patterns prevalent in Balinese tradition.

Across these diverse sources, we consistently observed several recurring layout
and visual features that directly informed our annotation schema:

<footnote n="4">⁴ www.creativecommons.org/licenses/by-nc-sa/3.0/</footnote>

<footnote n="5">⁵ www.creativecommons.org/licenses/by-nc-sa/4.0/</footnote>

<footnote n="6">⁶ www.creativecommons.org/licenses/by/4.0/</footnote>
**Textual Layouts.** Characterized by dense horizontal inscriptions, often organized into multi-line sequences and frequently grouped into structured paragraph blocks.

**Visual Complexity.** Many manuscripts incorporate ornamental borders, distinct symbolic glyphs (e.g., markers for sections or emphasis), and rich decorative illustrations integrated within or around the textual content.

**Degradation Patterns.** We identified various forms of physical damage, including circular holes (for binding strings), cracks, tears, environmental stains, and text fading due to centuries of age and handling.

Despite the inherent diversity in script forms and textual content, these manuscripts exhibit comparable overarching structural patterns that collectively enable a unified layout analysis approach, as illustrated in Fig. 2. To systematically capture this cross-script layout structure, **PALM-LAY** defines **seven key annotation categories**, each designed to identify specific visual and structural elements. Their characteristics are detailed in Table 1.

<figure><img src="image_5.png" /><figcaption>**Fig. 2.** Visualization of layout region detection on manuscript examples. This figure highlights typical layout characteristics for regions found in (a) South Asian manuscripts and (b) Southeast Asian manuscripts, demonstrating the application of defined layout categories (MainRegion, TextLineRegion, ParagraphRegion, SymbolicMark, PhysicalDamage, Illustration, and Other).</figcaption></figure>
<table><caption>**Table 1.** Definition of annotated layout categories in PALM-LAY dataset.</caption><thead><tr><td>Category</td><td>Description</td></tr></thead><tbody><tr><td>MainRegion</td><td>Primary content area; encompassing whole text block.</td></tr><tr><td>ParagraphRegion</td><td>Grouped blocks of related text lines.</td></tr><tr><td>TextLineRegion</td><td>Individual horizontal lines of text.</td></tr><tr><td>SymbolicMark</td><td>Symbolic marks, section dividers, and religious symbols.</td></tr><tr><td>PhysicalDamage</td><td>Regions of degradation (binding holes, cracks, fading).</td></tr><tr><td>Illustration</td><td>Visual depictions (deities, animals, cultural drawings).</td></tr><tr><td>Other</td><td>Non-original content (stamps, labels, modern notes) and unclassified elements.</td></tr></tbody></table>

By meticulously identifying and consistently labeling these diverse visual elements across all images, **PALM-LAY** enables standardized, fine-grained layout analysis across diverse palm leaf manuscript traditions, thereby supporting both comparative research and scalable computational document processing workflows.

# 4 Dataset Construction

<figure><img src="image_6.png" /><figcaption>**Fig. 3.** Annotation workflow across scripts, illustrating the stages of data selection, splitting, validation, and subsequent benchmarking experiments.</figcaption></figure>

The construction of the PALM-LAY dataset involved meticulous data acquisition, rigorous preprocessing, and a structured annotation process. As depicted in Fig. 3, our workflow began with collecting raw palm leaf manuscript images, followed by a thorough cleaning and selection phase to ensure high data quality suitable for layout analysis.

## 4.1 Data Collection and Preprocessing

We curated the PALM-LAY dataset from six major publicly available palm leaf manuscript collections. These foundational datasets originate from signifi-
cant cultural heritage digitization efforts across India, Cambodia, and Indonesia [9,29,24,16,8,10].

During the preprocessing phase, we meticulously selected only usable images for our layout analysis. Many raw datasets, not originally captured for document analysis, contained challenges such as multiple manuscripts in a single image, distracting backgrounds, or severe quality issues. Using manual processing with Photoshop<sup>7</sup>, we carefully cropped each image to isolate individual manuscripts and remove extraneous backgrounds or unwanted frames, ensuring that only clear and relevant content was retained.

A crucial observation during this phase was the content variation across collections. For instance, while most manuscripts contained diverse regions, collections such as Khmer, Jathakam, and Sundanese rarely included illustrations or complex drawings, as indicated by the annotation availability in Table 2. Therefore, these scripts are noted for the absence of illustration annotations in our dataset, reflecting the genuine characteristics of their source material rather than a skip in annotation. The preprocessed and cleaned images were then prepared for the subsequent annotation and splitting phases. Table 3 provides a concise summary of the script coverage and the total number of pages contributed by each source collection, totaling 566 pages.

<table><caption>**Table 2.** Layout annotation availability across palm leaf manuscript subsets.</caption><thead><tr><th>Category</th><th>Tamil Jathakam</th><th>Kambara-<br>mayanam</th><th>Khmer</th><th>Sundanese</th><th>Balinese</th></tr></thead><tbody><tr><td>MainRegion</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>ParagraphRegion</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>TextLineRegion</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>PhysicalDamage</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>SymbolicMark</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Illustration</td><td>Yes</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td></tr><tr><td>Other</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr></tbody></table>

## 4.2 Annotation Process

The annotation process was meticulously conducted using a combination of industry-standard tools. We primarily employed the VGG Image Annotator (VIA) [6] for its flexibility in supporting bounding box and polygon annotations, and Roboflow<sup>8</sup> for dataset management, versioning, and integration with downstream training pipelines. Photoshop was also utilized for initial preprocessing tasks, such as image enhancement and alignment, prior to annotation.

<footnote n="7"><sup>7</sup> www.adobe.com/products/photoshop</footnote>

<footnote n="8"><sup>8</sup> www.roboflow.com</footnote>
<table><caption>**Table 3.** Distribution of PALM-LAY dataset across collections and train/test splits.</caption><thead><tr><th>Collection</th><th>Total Pages Train (80%)</th><th>Total Pages Test (20%)</th></tr></thead><tbody><tr><td>Tamil [8]</td><td>101</td><td>81</td><td>20</td></tr><tr><td>Jathakam [16]</td><td>108</td><td>86</td><td>22</td></tr><tr><td>Kambaramayanam [16]</td><td>41</td><td>33</td><td>8</td></tr><tr><td>Khmer [29,10]</td><td>155</td><td>124</td><td>31</td></tr><tr><td>Balinese [9,10]</td><td>100</td><td>80</td><td>20</td></tr><tr><td>Sundanese [24,10]</td><td>61</td><td>49</td><td>12</td></tr></tbody><tfoot><tr><td><strong>Overall Total</strong></td><td><strong>566</strong></td><td><strong>453</strong></td><td><strong>113</strong></td></tr></tfoot></table>

As visually summarized in Fig. 4, our annotation workflow followed a multi-step process. Each image was annotated manually by a dedicated team, using bounding boxes for general layout components and polygons for complex or irregular regions, including curved text lines and intricate damage marks. This hybrid approach was chosen to balance annotation precision with compatibility across object detection models.

The annotation team consisted of trained students and researchers from Cambodia, China, and Indonesia, whose cultural and linguistic familiarity with the respective scripts significantly improved annotation quality. To ensure label consistency and minimize subjectivity, we implemented a rigorous two-phase validation process: one team performed the initial annotations, and a second independent team reviewed and corrected them through a detailed validation pass.

Although our process emphasized careful quality control, we acknowledge that we have not yet reported formal inter-annotator agreement metrics. Metrics such as Cohen's Kappa or IoU-based agreement scores will be computed and included in future dataset releases to further validate the annotation protocol and quantify consistency. Nonetheless, this multi-layered annotation and review strategy has proven effective in achieving high-quality labels across culturally diverse manuscript traditions.

In total, the dataset comprises 566 pages annotated across six manuscript collections. As summarized in Table 4, these include 6,334 annotated instances spanning seven layout categories. It is worth noting that the *Illustration* category is underrepresented or absent in manuscripts from Jathakam, Khmer, and Sundanese collections, reflecting script- and region-specific visual characteristics.

# 5 Benchmarking Experiments

## 5.1 Experimental Setup

Our benchmarking experiments were designed to comprehensively evaluate object detection models on the **PALM-LAY dataset**. We structured our evaluation along two primary dimensions:
<figure><img src="image_7.png" /><figcaption>**Fig. 4.** Visualization of layout region detection in a manuscript annotation example. This illustration highlights typical annotated layout characteristics for various region layout categories.</figcaption></figure>

<table><caption>**Table 4.** Total number of annotated regions per layout category.</caption><thead><tr><th>Category</th><th>Annotated Regions</th></tr></thead><tbody><tr><td>MainRegion</td><td>566</td></tr><tr><td>ParagraphRegion</td><td>1,388</td></tr><tr><td>TextLineRegion</td><td>2,735</td></tr><tr><td>SymbolicMark</td><td>254</td></tr><tr><td>PhysicalDamage</td><td>1,168</td></tr><tr><td>Illustration</td><td>66</td></tr><tr><td>Other</td><td>157</td></tr><tr><td><strong>Total</strong></td><td><strong>6,334</strong></td></tr></tbody></table>

1. **Script-specific detection performance:** This involved evaluating the models' performance on individual script-specific datasets, analyzing results across all seven layout categories.

2. **Cross-script generalization performance:** For this, models were trained on the entire combined cross-script training dataset and evaluated on the entire combined test dataset. This dual reporting strategy allows for both a generalized understanding of model capabilities and a granular identification of script-specific performance nuances.

We trained and rigorously evaluated a diverse set of state-of-the-art object detection models, representing prominent paradigms in the field: the efficient, one-stage YOLO series (specifically YOLOv8, YOLOv9, and YOLOv11 [17,30,11]) known for their speed, and the transformer-based detectors (DETR [2] and its robust variant RF-DETR [19]) renowned for their global context understanding and end-to-end nature. This selection allows for a comprehensive assessment of different model capabilities on this challenging dataset.
## 5.2 Training Settings

<table><caption>**Table 5.** Detailed detection performance (mAP@0.5) per layout category for each Script. Results are from models trained on script-specific datasets.</caption><thead><tr><th>Script</th><th>Category</th><th>YOLOv8</th><th>YOLOv9</th><th>YOLOv11</th><th>DETR</th><th>RF-DETR</th></tr></thead><tbody><tr><td rowspan="5">Tamil</td><td>MainRegion</td><td>0.929</td><td>0.915</td><td>0.909</td><td>0.891</td><td>0.961</td></tr><tr><td>ParagraphRegion</td><td>0.995</td><td>0.993</td><td>0.994</td><td>0.979</td><td>0.995</td></tr><tr><td>TextLineRegion</td><td>0.666</td><td>0.672</td><td>0.584</td><td>0.934</td><td>0.972</td></tr><tr><td>SymbolicMark</td><td>0.444</td><td>0.515</td><td>0.582</td><td>0.139</td><td>0.551</td></tr><tr><td>PhysicalDamage</td><td>0.935</td><td>0.959</td><td>0.979</td><td>0.886</td><td>0.923</td></tr><tr><td rowspan="6">Jathakam</td><td>MainRegion</td><td>0.942</td><td>0.951</td><td>0.937</td><td>0.995</td><td>0.995</td></tr><tr><td>ParagraphRegion</td><td>0.939</td><td>0.930</td><td>0.929</td><td>0.995</td><td>0.995</td></tr><tr><td>TextLineRegion</td><td>0.958</td><td>0.960</td><td>0.932</td><td>0.863</td><td>0.952</td></tr><tr><td>SymbolicMark</td><td>0.162</td><td>0.264</td><td>0.105</td><td>0.604</td><td>0.990</td></tr><tr><td>Illustration</td><td>0.332</td><td>0.000</td><td>0.332</td><td>0.784</td><td>0.877</td></tr><tr><td>PhysicalDamage</td><td>0.374</td><td>0.357</td><td>0.398</td><td>0.885</td><td>0.894</td></tr><tr><td rowspan="6">Kambaramayanam</td><td>MainRegion</td><td>0.878</td><td>0.889</td><td>0.995</td><td>0.995</td><td>0.995</td></tr><tr><td>ParagraphRegion</td><td>0.975</td><td>0.995</td><td>0.995</td><td>0.933</td><td>0.995</td></tr><tr><td>TextLineRegion</td><td>0.803</td><td>0.828</td><td>0.769</td><td>0.787</td><td>0.948</td></tr><tr><td>SymbolicMark</td><td>0.547</td><td>0.527</td><td>0.551</td><td>0.574</td><td>0.573</td></tr><tr><td>PhysicalDamage</td><td>0.995</td><td>0.995</td><td>0.995</td><td>0.867</td><td>0.901</td></tr><tr><td rowspan="6">Khmer</td><td>MainRegion</td><td>0.995</td><td>0.995</td><td>0.995</td><td>0.995</td><td>0.948</td></tr><tr><td>ParagraphRegion</td><td>0.995</td><td>0.994</td><td>0.983</td><td>0.995</td><td>0.941</td></tr><tr><td>TextLineRegion</td><td>0.792</td><td>0.856</td><td>0.889</td><td>0.935</td><td>0.910</td></tr><tr><td>SymbolicMark</td><td>0.793</td><td>0.823</td><td>0.811</td><td>0.505</td><td>0.729</td></tr><tr><td>PhysicalDamage</td><td>0.820</td><td>0.868</td><td>0.809</td><td>0.822</td><td>0.861</td></tr><tr><td rowspan="6">Balinese</td><td>MainRegion</td><td>0.963</td><td>0.975</td><td>0.959</td><td>0.950</td><td>0.961</td></tr><tr><td>ParagraphRegion</td><td>0.931</td><td>0.950</td><td>0.907</td><td>0.914</td><td>0.918</td></tr><tr><td>TextLineRegion</td><td>0.759</td><td>0.800</td><td>0.735</td><td>0.847</td><td>0.856</td></tr><tr><td>SymbolicMark</td><td>0.328</td><td>0.463</td><td>0.304</td><td>0.703</td><td>0.618</td></tr><tr><td>PhysicalDamage</td><td>0.969</td><td>0.897</td><td>0.925</td><td>0.830</td><td>0.859</td></tr><tr><td>Illustration</td><td>0.761</td><td>0.858</td><td>0.724</td><td>0.680</td><td>0.799</td></tr><tr><td rowspan="4">Sundanese</td><td>MainRegion</td><td>0.995</td><td>0.995</td><td>0.995</td><td>0.995</td><td>0.945</td></tr><tr><td>ParagraphRegion</td><td>0.995</td><td>0.995</td><td>0.995</td><td>0.995</td><td>0.995</td></tr><tr><td>TextLineRegion</td><td>0.951</td><td>0.984</td><td>0.888</td><td>0.970</td><td>0.964</td></tr><tr><td>SymbolicMark</td><td>0.384</td><td>0.743</td><td>0.626</td><td>0.017</td><td>0.326</td></tr><tr><td></td><td>PhysicalDamage</td><td>0.605</td><td>0.752</td><td>0.514</td><td>0.352</td><td>0.582</td></tr></tbody></table>

The following configurations and strategies were consistently applied across all benchmarked models:

**Pretrained Backbone.** For transformer-based models such as DETR [2] and RF-DETR [19], a ResNet-50 convolutional backbone was used for feature extraction, initialized with ImageNet-pretrained weights. These visual features
<table><caption>**Table 6.** Cross-script performance (mAP@50) for each layout category across different scripts. Results are based on PALM-LAY models evaluated on the entire combined dataset.</caption><thead><tr><th>Category</th><th>YOLOv8</th><th>YOLOv9</th><th>YOLOv11</th><th>DETR</th><th>RF-DETR</th></tr></thead><tbody><tr><td>MainRegion</td><td>0.975</td><td>0.976</td><td>0.948</td><td>0.969</td><td>0.972</td></tr><tr><td>ParagraphRegion</td><td>0.983</td><td>0.981</td><td>0.976</td><td>0.977</td><td>0.974</td></tr><tr><td>TextLineRegion</td><td>0.900</td><td>0.900</td><td>0.880</td><td>0.898</td><td>0.945</td></tr><tr><td>SymbolicMark</td><td>0.547</td><td>0.565</td><td>0.541</td><td>0.533</td><td>0.558</td></tr><tr><td>PhysicalDamage</td><td>0.803</td><td>0.790</td><td>0.773</td><td>0.869</td><td>0.827</td></tr><tr><td>Illustration</td><td>0.586</td><td>0.708</td><td>0.447</td><td>0.664</td><td>0.651</td></tr><tr><td>Other</td><td>0.919</td><td>0.922</td><td>0.907</td><td>0.875</td><td>0.893</td></tr></tbody></table>

are then processed by a transformer encoder-decoder for object detection. For the YOLO series [17,30,11], we employed each model's default backbone architecture, pre-trained on the COCO dataset, which is optimized for real-time performance and lower-resource training.

**Framework.** All training processes were implemented by our team from scratch using PyTorch, providing full control over the experimental pipeline and enabling tailored optimization. We utilized the annotated PALM-LAY dataset, with its detailed region statistics as presented in Table 3, for all model training and evaluation.

**Training Strategy.** The dataset was partitioned into 80% for training and 20% for testing. This split was carefully stratified by script to ensure that all six manuscript traditions were well represented in both subsets, thereby supporting robust cross-script generalization. Input images were resized to 800×200 pixels. Models were trained for 100 epochs using a learning rate of 1e-4 and a batch size of 8. To expand the dataset size by a factor of 3 to 5, standard data augmentation techniques were applied, including random flipping, rotation, brightness and greyscale adjustments, and exposure changes. These augmentations were designed to improve model generalization and robustness. The AdamW optimizer was used for training.

**Evaluation Metrics.** Performance was systematically assessed using standard object detection metrics [17]: Mean Average Precision (mAP) at an Intersection over Union (IoU) threshold of 0.5 (mAP@0.5).

## 5.3 Results

## 5.4 Benchmarking Analysis

In this section, we present benchmarking results along two primary dimensions. First, we evaluate **script-specific detection performance** by analyzing how each model performs on individual subsets of the test set, disaggregated across the seven defined layout categories. As shown in Table 5, we consistently observe
that *MainRegion*, *TextLineRegion*, and *ParagraphRegion* achieve notably higher
detection accuracy than other categories. This suggests that models are more ef-
fective at identifying primary and spatially prominent layout components, which
tend to have clearer structure and larger region areas.

Interestingly, we find that the YOLOv8-v11) consistently out-
performs Transformer-based models such as DETR and RF-DETR on categories
with relatively fewer and smaller instances, such as *SymbolicMark*, *Physical-
Damage*, and *Other*. However, for categories with more frequent and larger
annotated instances—such as *MainRegion*, *TextLineRegion*, and *ParagraphRe-
gion*—Transformer-based detectors demonstrate competitive or even superior
performance. This pattern aligns with expectations in low-resource settings like
PALM-LAY, where convolutional models benefit from strong inductive biases
and faster convergence, while Transformer-based models often require more ex-
tensive training data and epochs to reach their full potential.

Second, we assess **cross-script generalization** by training each model on the combined multi-script dataset and evaluating it on the unified test set. As outlined in Table 6, this setup allows us to analyze each model’s ability to adapt across different scripts, each with unique visual and structural properties. Notably, combining scripts during training improves performance for underrepresented categories such as *Illustration*, *SymbolicMark*, and *Other*, suggesting beneficial cross-script feature learning.

This dual evaluation strategy provides both high-level insights into general
layout detection capabilities and fine-grained understanding of script-specific
model behavior. Overall, results indicate that modern object detection models
can perform robustly even on complex and diverse palm leaf manuscripts—especially
when supported by consistent annotation schemas and a well-curated dataset.

5.5 Discussion and Limitations

The comprehensive benchmarking results offer valuable insights into the capa-
bilities and limitations of state-of-the-art object detection models when applied
to the complex task of layout analysis in historical palm leaf manuscripts.

**Overall Performance and Model Comparison.** Among all evaluated models, the *MainRegion*, *TextLineRegion*, and *ParagraphRegion* categories consistently achieved the highest results. This strong performance can be attributed to their consistent visual patterns, larger average bounding box sizes, and higher frequency within the dataset. These robust results highlight the feasibility of applying layout analysis techniques to effectively extract foundational structural elements from palm leaf manuscripts, paving the way for future research in dataset augmentation and downstream tasks such as script classification and text recognition.

**Detection Challenges for Specific Categories.** In contrast, the *PhysicalDamage*, *SymbolicMark*, and *Other* categories exhibited consistently lower AP scores. These challenges are primarily due to their high visual variability, smaller object sizes, and significant ambiguity in visual representation. For example, *SymbolicMark* elements often differ in style and meaning across cultural
regions, making generalization more difficult. The *Other* category, which encompasses heterogeneous modern annotations (e.g., stamps, Latin-script notes, archival marks), introduces further complexity due to diverse fonts, orientations, partial occlusions, and multi-language elements.

**Cross-Script Generalization and Nuances.** The combined training strategy successfully enabled cross-script generalization, demonstrating that a unified model can handle multiple palm leaf traditions with reasonable effectiveness. While overall results were promising, performance variations were observed across individual scripts. Notably, cross-script training improved results for underrepresented categories, likely due to shared visual and structural characteristics across scripts. This suggests that while large, multi-script pre-trained models are viable—particularly for low-resource scripts with limited data—there remains potential to improve performance through script-specific fine-tuning and targeted augmentation.

**Limitations.** This study focuses exclusively on region-level layout detection and does not yet extend to multimodal tasks involving textual or semantic content. A significant limitation is the lack of accurate OCR transcriptions and aligned token-level ground truth across the diverse scripts found in palm leaf manuscripts. This constraint currently limits the applicability of models such as LayoutLM or Donut, which require structured text and semantic annotations for training. Additionally, some subsets of the dataset remain relatively small, highlighting the need for data augmentation strategies and the inclusion of additional sources to improve model robustness and generalization.

Despite these limitations, we consider *PALM-LAY* a foundational resource. With future enhancements—including OCR transcriptions, glyph-level alignments, and semantic labels—the dataset could be extended to support a range of downstream multimodal tasks, such as named entity recognition, semantic region classification, and document visual question answering in historical manuscript analysis.

**Future Directions.** Future work will focus on expanding the dataset with more comprehensive annotations, further optimizing models for challenging categories and script-specific nuances, and exploring end-to-end multimodal pipelines for practical application in ancient manuscript analysis and digital humanities.

# 6 Conclusion

In this paper, we introduced **PALM-LAY**, the first unified, cross-regional dataset specifically designed for comprehensive layout analysis of historical palm leaf manuscripts. The dataset comprises 566 manuscript pages from six distinct collections, meticulously annotated across seven layout categories, totaling over 6,000 region-level annotations. PALM-LAY addresses critical gaps in existing resources by providing consistent and detailed annotations across diverse scripts, thereby enabling robust benchmarking and comparative analysis. Through extensive experiments using state-of-the-art object detection models, we demonstrated the feasibility of cross-script generalization while highlighting persistent
challenges—particularly in visually complex or low-frequency categories such as
symbolic marks and physical damage. These findings underscore the potential of
automated layout analysis to support the preservation and digital accessibility
of these invaluable cultural artifacts. As a foundational step toward the broader
goal of semantic document understanding, this work focuses on layout region
detection as a prerequisite for downstream tasks. Looking ahead, we plan to
enrich PALM-LAY with OCR transcriptions, token-level annotations, and se-
mantic labels. Such extensions will pave the way for deeper cultural insights and
more inclusive digital preservation efforts across low-resource and historically
significant scripts.

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