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.
Abstract
Handwritten Urdu text recognition remains a very challenging problem due to the cursive and complex nature of the Nastaliq script. Each writer has a unique style, and there is a severe lack of large, well-labeled datasets. These challenges make Urdu text recognition much harder than for many other languages. Here we propose a novel and effective approach for handwritten Urdu word recognition that addresses both data scarcity and feature complexity. We utilized conditional diffusion models to generate synthetic handwritten Urdu word images. These models produce realistic word samples covering a wide range of variations, such as stroke thickness, curves, and writing styles. The synthetic images augment the training data, helping the recognition model learn better in low-resource settings. For recognition, we employ a Neighborhood Attention Transformer (NAT) to extract robust visual features from both real and generated images. The visual embeddings are then fed into a pretrained BART language model configured as an encoder–decoder, which predicts the correct Urdu word. In this work, we first construct a baseline handwritten Urdu word dataset, addressing the lack of publicly available dataset for the text recognition. Building upon this foundational dataset, we leverage diffusion based generative approach to generate diverse and realistic synthetic handwritten samples, significantly alleviating the challenges of manual data collection. To the best of our knowledge, this is the first study to introduce both a real handwritten Urdu word dataset and a diffusion-generated synthetic dataset. In addition, we develop a unified word recognition model trained jointly on handwritten and printed Urdu word data, leading to improved recognition robustness and performance.
Although exact word-level recognition remained difficult because of the extremely limited dataset size, the proposed framework successfully learned meaningful sequential patterns and produced increasingly structured Kirundi-like predictions.
Niyifasha Patrick· International journal of re...· 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
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
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.
Bilal Abdulrahman, Farhan Mohamed· Journal of Human Centered Te...· 0 citations
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 Marathi character recognition is still kind of hard research problem because there is so much variability within the same class ,and between classes they can look a bit similar ,also the strokes are complex and different people write in their own style. A lot of CNN and Transformer like methods either do not really capture long range relationships well enough, or they end up being too heavy computationally, you know not so efficient. So in this paper we suggest a Hybrid Vision Mamba and Transformer (HVMT), framework for stronger offline handwritten Marathi character recognition. The HVMT idea combines the hierarchical feature extraction power of Vision Mamba, which uses selective state-space modeling, with the contextual representation learning of a smaller Transformer encoder, and inside that encoder we use Multi-Head Self-Attention. Experiments are done on the public MHCD_GIETV2 dataset, where handwritten Marathi characters are collected from writers in different age groups and with diverse writing styles. Before training the images are turned into grayscale, then normalized, resized, and also augmented, to help the model generalize better. The proposed HVMT is compared with CNN, ResNet-50, EfficientNet-B0, ConvNeXt-Tiny, Vision Transformer (ViT-B/16), Swin Transformer-Tiny, and Vision Mamba, all under the same experimental setup. Experimental results show that the proposed framework achieved accuracy 87.11% , precision 87.09% , recall 87.10% and F1-score 87.09% which is better than the compared architectures. At the same time it only uses 26.9 million parameters, 2.6 GFLOPs, and inference time 1.305 ms per image. In other words, the HVMT framework seems to strike a workable tradeoff between recognition precision and compute efficiency. Because of this it is a good fit for things like intelligent document analysis, handwritten document digitization , archival preservation , and several other Indic script recognition tasks and more.
S. Khandakhani, Sachikanta Dash, Sasmita Padhy et al.· Journal of Intelligent Decis...· 0 citations