Aug 2026· Journal of Human Centered Technology· 0 citations· 43 references
TL;DR
An edge-aware line-level HTR framework that extends a CNN-Transformer baseline with a learnable edge-extraction channel and Squeeze-and-Excitation channel attention and shows that combining learnable structural cues with channel-wise attention has improved robustness for degradation-prone historical manuscript collections.
Abstract
Recent Handwritten Text Recognition (HTR) systems for Arabic and other right-to-left historical manuscripts have advanced exploration through convolutional neural network (CNN), recurrent, and Transformer-based models. However, degradation, weak diacritics, unstable baselines, and visually similar cursive letterforms still limit the recognition robustness. This paper presented an edge-aware line-level HTR framework that extends a CNN-Transformer baseline with a learnable edge-extraction channel and Squeeze-and-Excitation (SE) channel attention. The edge module emphasized stroke boundaries and character contours, while SE attention recalibrated feature responses to suppress background artifacts and preserve informative ink patterns. The resulting sequence was modeled by a Transformer encoder and trained using Connectionist Temporal Classification (CTC) with an auxiliary decoder cross-entropy loss. The experiments on the Kalima Arabic manuscript line-image dataset, using Books 1-8 with 86 pages and 1,759 annotated text lines, reduced character error rate from 6.40% to 4.10% and word error rate from 27.43% to 20.43%. These results show that combining learnable structural cues with channel-wise attention has improved robustness for degradation-prone historical manuscript collections.
Offline handwritten word recognition has progressed from whole-word classification to sequence transcription, yet many systems depend on large annotated corpora and exploit lexical regularities over explicit character evidence. This paper presents an alternative formulation for Arabic offline handwritten word recognition, treating characters as spatial objects detected via a Faster Region-Based Convolutional Neural Network rather than symbols generated by a one-dimensional decoder. We construct and release a character-level annotated subset of 2153 handwritten word images from a standard Arabic benchmark, exporting matched detection, sequence, and word-class labels. We also introduce an open-source subword exchange toolkit that creates a controlled structural-generalization benchmark by swapping subwords while preserving handwriting style. Experiments compare the proposed detector against whole-word and sequence-based baselines on both the original held-out split and the perturbed benchmark. Results show sequence models degrade sharply under structural recombination, whereas the proposed detector remains stable, achieving a 26.56% character error rate and 70.0% word accuracy on the perturbed benchmark. These findings demonstrate that explicit character localization provides a robust, data-efficient alternative for Arabic handwritten text recognition in low-resource settings.
Sofiane Medjram, Ruwaidah Saud Alnejaidi· Applied Sciences· 0 citations
Offline handwritten text recognition (HTR) remains one of the most challenging problems in document image analysis because of cursive writing, character overlap, unconstrained writing styles, long inter-character dependencies and background degradation. Classical CNN-BLSTM pipelines and single-scale attention models extract features at a fixed resolution and therefore struggle to jointly represent thin strokes of small characters and the global shape of large or connected characters. In this paper we propose a Multi-Scale Transformer-Based Lexicon-Guided HTR framework built around an Adaptive Feature Fusion (AFF) mechanism. A multi-scale convolutional backbone extracts shallow, middle and deep feature maps using parallel 3×3, 5×5 and 7×7 receptive fields. The AFF module learns content-dependent soft weights and channel attention to fuse these heterogeneous features into a single scale-balanced representation, replacing the fixed feature map used by previous lexicon-and-attention systems. A Transformer encoder with multi-head self-attention then models long-range context far more effectively than bidirectional recurrent layers. The network is trained end-to-end with the Connectionist Temporal Classification (CTC) objective and decoded with a lexicon-guided beam search that combines an n-gram prior with edit-distance dictionary matching. On the IAM line-level benchmark the proposed model attains a character error rate (CER) of 3.08% and a word error rate (WER) of 7.24%, improving over our lexicon-and-attention baseline (4.15% CER / 9.72% WER) by 25.8% and 25.5% relative, respectively. On RIMES we obtain 2.71% CER / 7.92% WER, and on the George Washington collection 5.82% CER / 13.05% WER, again outperforming the baseline.
Lalita Kumari· International journal on eme...· 0 citations
Recognition of Indic handwritten text is a difficult issue with the complex formations of characters, variability of graphemes, ambiguity of strokes, and significant differences between writers. This is particularly problematic in scripts (such as Tamil and Kannada) where the form of the handwritten words and the composition structure often are not regular. Although recent CNN-RNN and CNN-Transformer architecture have achieved encouraging results, they either pay much attention to local visual representation or global context representation and do not consider the structural relationship of handwritten patterns at an appropriate level. This work will offer a solution to this drawback by suggesting a Hybrid CNN 10 Capsule 10 Transformer network with Connectionist Temporal Classification (CTC) to perform end-to-end handwritten word recognition. The proposed framework uses CNN layers to extract local visual features, the capsule module to encode structural and compositional relationships, and the Transformer to learn long-range sequence dependencies to be correctly transcribed. The model is tested on Tamil and Kannada handwritten data to check the effectiveness of cross-scripts. The results of the experiment indicate that the given architecture has a test accuracy of 85.46 percent and a Character Error Rate (CER) of 0.0281 on Tamil and 88.70 percent and a Character Error Rate (CER) of 0.0175 on Kannada. These results indicate that the hybrid framework proposed enhances the performance of cross-script handwritten text recognition as compared to baseline architectures.
A S Manjunath, Umesh Dadadahalli Ramu, Madhusudan G et al.· International journal of com...· 0 citations
This study presents an advanced framework for Telugu handwritten character recognition by integrating Conditional Generative Adversarial Networks (cGANs) with Vision Transformer (ViT) architectures. Critical issues with Telugu scripts, such as intricate character structures, significant inter-writer variability, and a lack of annotated handwritten data, are addressed by the suggested method. While the Vision Transformer utilizes self-attention mechanisms to capture long-range spatial dependencies and global contextual features necessary for accurate recognition, cGAN-based synthetic data augmentation is employed to enhance dataset diversity and mitigate class imbalance. The proposed system outperforms several current CNN-, RNN-, and heuristic-based techniques, achieving character recognition accuracy of 97.89% and word recognition accuracy of 97.34%, as determined through extensive experiments conducted on real and synthetic handwritten datasets. Stable performance under noisy and real-world conditions is further confirmed by robustness analysis. The outcomes confirm the efficacy of integrating transformer-based learning with generative AI, creating a dependable and scalable OCR solution for low-resource Indic scripts, such as Telugu.
Padmavathi Pragada, D. Ch· Engineering Research Express· 0 citations
With the increasing demand for reusing paper documents in educational and office settings, accurate segmentation of handwritten and printed text has become a crucial step in document digitization. Although numerous deep learning models have been developed for this task, their high computational cost limits deployment on resource-constrained edge devices. To address this challenge, we present a lightweight framework optimized for efficient performance on devices with severely limited computational capacity. Our approach begins with the Sentence-level Connected Component Segmentation algorithm, aimed at extracting coherent sentence-level segments from document images. We then design a novel Region-aware Handwriting Descriptor (RHD) to capture the intrinsic variability of human handwriting at the sentence level. A simple conventional classifier can then be seamlessly integrated with our designed descriptor, demonstrating strong classification performance for distinguishing handwritten and printed sentence-level text images, highlighting that the proposed descriptor is agnostic to the choice of classifier. Extensive experiments are performed on our self-constructed Multilingual High-Quality Annotated Dataset for Handwritten and Printed Text Segmentation (MAD-HPTS) and a public benchmark PHD-AS, and the experimental results demonstrate that the proposed framework outperforms current state-of-the-art methods in both accuracy and computational efficiency. On MAD-HPTS, our method sacrifices only 1.4% accuracy compared to the leading deep neural network baseline, yet achieves more than 8 times speedup in inference, making it well-suited for lightweight deployment.
Zhixian Lu, Jianwei Zhang, Lei Zhang et al.· 0 citations
This is the first study to introduce both a real handwritten Urdu word dataset and a diffusion-generated synthetic dataset, and develops a unified word recognition model trained jointly on handwritten and printed Urdu word data, leading to improved recognition robustness and performance.
Wahid Hussain, Shahbaz Hassan, I. Hassan et al.· IEEE Access· 0 citations