Text line segmentation in Sanskrit manuscripts remains a difficult problem due to physical degradation, irregular layouts, and script-specific features such as the Shirorekha (the continuous headline) and dense conjuncts. We present ShirorekhaNet, a residual encoder-decoder network that performs pixel-accurate line-level segmentation. The architecture accepts a four-channel input-RGB combined with a binarised image-and integrates multi-scale Inception (MSI) modules with channel attention, residual connections with learnable gating, and a hybrid training curriculum that combines cosine learning-rate decay with a Proportional-Integral-Derivative (PID) controller. The model is trained on an augmented set of 8585 images derived from 85 annotated folios. On the 25 original, unaugmented test images, ShirorekhaNet (PID + cosine) achieves an IoU of 0.9075, a Dice of 0.9513, a Shirorekha accuracy of 95.68
Subgraph-level reasoning is increasingly important for analyzing localized structures within large heterogeneous graphs. However, existing benchmarks are mostly node-centric, edge-centric, or graph-level, with limited support for subgraph-level supervision, query semantics, or spotting objectives. Moreover, analyzing large heterogeneous networks can be computationally costly, especially in settings with limited resources. This work introduces the HSB-GDL benchmark dataset to address these gaps by supporting subgraph-level learning and detection in academic heterogeneous graphs with configurable complexity and evaluation depth, without requiring advanced computational resources. The benchmark is based on the OGBN-MAG dataset and includes a diverse set of 59,585 subgraphs spanning both homogeneous and heterogeneous structures. These include ten types of real-world patterns and six types of controlled synthetic patterns. The subgraphs are labeled, annotated, and organized for simple integration. Classification is evaluated under complete-standard and strict node-disjoint protocols. Query spotting is assessed through global retrieval over the benchmark catalog and conditional hard-negative ranking. The evaluation includes feature controls and GIN, RGCN, and TransformerConv baselines. We provide a detailed empirical evaluation that includes structural-bias diagnostics and stronger graph-learning baselines. The diagnostic results show that HSB-GDL contains both easily separable calibration patterns and more ambiguous real heterogeneous subgraphs. In particular, node/edge-type structure is a strong predictive signal for several classes. Under the complete-standard protocol, GIN achieves a Macro-F1 of 90.95± 0.53% , compared with 60.77± 0.96% for the node-type-only control. Under strict node disjointness, GIN obtains 90.45± 0.34% Macro-F1. For global query retrieval, designated-target Recall@100 is 61.25% , while valid-occurrence Recall@100 is bounded at 86.13– 97.88% . In conditional hard-negative ranking, the combined ranker achieves MRR 0.8710. These findings indicate that HSB-GDL serves as a subgraph-level benchmark with diagnostic capability, enabling researchers to distinguish shortcut-driven classification from topology-aware and relation-aware subgraph reasoning. The benchmark is open source, CPU-accessible, and extensible, making it useful for reproducible research on heterogeneous graph learning, subgraph-level pattern recognition, and query-oriented graph retrieval for research and educational purposes.
Child handwriting enhancement poses unique challenges distinct from other document restoration tasks. Unlike the global degradation observed in historical manuscripts, children’s handwriting exhibits locally inconsistent faint strokes, misalignment with the writing line, and behavioral artifacts such as scribbles or partial erasures. When scanned, these irregularities are compounded by background noise, illumination variation, and scanner-induced distortions, deforming vital stroke information. Existing document restoration models, optimized for high-contrast ink or uniform degradations, tend to erase these subtle pencil traces, severely limiting their utility for downstream child handwriting analysis. To address these issues, we present TraceNet, an application-driven restoration network designed specifically for faint and irregular child handwriting. TraceNet integrates Swin Transformer-based self-attention within a single deterministic encoder-decoder restoration framework. Temporal diffusion embeddings are used as a conditioning signal to adapt restoration strength according to degradation severity, enabling selective recovery of faint strokes and line removal without over-smoothing. Evaluations on a private corpus of child handwriting show that TraceNet achieves strong performance across image-quality and downstream HTR metrics over state-of-the-art baselines. An additional evaluation on the DIBCO dataset further shows that the same framework is effective for historical document enhancement. Remarkably, the model maintains high restoration quality even under few-shot fine-tuning with only a small set of annotated samples, highlighting its efficiency and adaptability for real-world educational, archival, and behavioral handwriting analysis tasks.
Writer identification plays an important role in forensic document examination, authentication, and handwriting analysis. This study presents a hyperspectral imaging (HSI)-based writer identification framework that combines Principal Component Analysis (PCA), machine learning, and deep learning for robust writer recognition under previously unseen pen conditions. Experiments were conducted on two hyperspectral handwriting datasets: iVision HHID (54 writers; 512 × 650 × 149 cubes spanning 478–900 nm) and UWA WIHSI (7 writers; 480 × 752 × 33 cubes spanning 400–720 nm). Sauvola thresholding, binary image subtraction, and spectral segmentation were employed to isolate handwritten regions and extract ink-pixel spectral signatures. PCA was applied as a denoising and reconstruction technique, with hyperspectral cubes reconstructed using the first two principal components ( K=2 ) to enhance writer-discriminative spectral information while suppressing noise. The discriminative capability of the extracted features was further validated through ANOVA, where significant spectral bands exhibited high F-values and low p-values. Split-based experiments were first conducted to identify suitable classification models, with Linear Discriminant Analysis (LDA) and one-dimensional Convolutional Neural Networks (CNNs) demonstrating the strongest performance. These classifiers were subsequently evaluated using strict pen-level blind testing protocols. For the iVision HHID dataset, four train–test pen-ratio configurations (4:1, 3:2, 2:3, and 1:4) and an additional 1:5 protocol were considered, while the UWA WIHSI dataset was evaluated using the 4:1 pen-blind protocol. PCA-LDA achieved a maximum accuracy of 99.84
With the rapid development of computer vision technology, end-to-end text detection and recognition have effectively alleviated the error accumulation and suboptimal performance problems in traditional two-step methods. Due to the excellent modular characteristics of the two-step method, it is still widely favored in many competitions and practical scenarios. This paper proposes an innovative solution, a scene text detection and recognition algorithm based on connectors and multi-level adapters. It retains the core advantages of modularity while addressing error accumulation in the two-step method and improving performance. We use well-trained high-precision detectors and recognizers, locking them to preserve their learned capabilities. Then, a connector is used to link the detector and recognizer. Since the locked detector and recognizer cannot learn features through end-to-end optimization, we design multi-level adapters to facilitate feature learning between them. Experimental verification shows that the connector-based scene text detection and recognition method achieves significant results on multiple public datasets. The word-level end-to-end spotting score of this method reaches 69.3
