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Handwriting Recognition in Low-resource Scripts using Adversarial Learning
Ayan Kumar Bhunia, Abhirup Das, Ankan Kumar Bhunia, Perla Sai Raj Kishore, Partha Pratim Roy
TL;DR
Limited training data and irregular handwriting make word recognition and spotting difficult in low-resource scripts, while generic augmentation may not cover the full range of variation. The paper adds adversarial feature deformation to existing retrieval frameworks. The enhanced systems improve recognition and spotting performance and show stronger gains in low-data settings.
Problem
Low-resource scripts have limited training data, and generic image augmentation may fail to cover handwriting’s complex shapes and uncommon variations.
Source: §1 · p. 1Method
The AFDM is inserted after an intermediate task-network layer to apply adversarial spatial transformations to features for word recognition or spotting.
Source: §1 · p. 2, §4.1 · p. 3Results
AFDM-enhanced frameworks improve word-recognition and spotting performance and generalize better to real-world handwritten data, with greater gains in low-data settings.
Source: §1 · p. 2, §5.5 · p. 8Takeaways & Limitations
Adversarial feature-space warping with TPS is presented as a scalable approach to address the shortage of handwriting variations in sparse datasets.
Source: §6 · p. 8
Abstract
from arXiv · showhide
Handwritten Word Recognition and Spotting is a challenging field dealing with handwritten text possessing irregular and complex shapes. The design of deep neural network models makes it necessary to extend training datasets in order to introduce variations and increase the number of samples; word-retrieval is therefore very difficult in low-resource scripts. Much of the existing literature comprises preprocessing strategies which are seldom sufficient to cover all possible variations. We propose the Adversarial Feature Deformation Module (AFDM) that learns ways to elastically warp extracted features in a scalable manner. The AFDM is inserted between intermediate layers and trained alternatively with the original framework, boosting its capability to better learn highly informative features rather than trivial ones. We test our meta-framework, which is built on top of popular word-spotting and word-recognition frameworks and enhanced by the AFDM, not only on extensive Latin word datasets but also sparser Indic scripts. We record results for varying training data sizes, and observe that our enhanced network generalizes much better in the low-data regime; the overall word-error rates and mAP scores are observed to improve as well.
1. Introduction
Handwritten word recognition and spotting remain difficult in low-resource scripts, where limited data and irregular handwriting challenge deep-learning systems. The paper proposes adversarial feature-space deformation to improve learning from scarce examples.
1. Introduction
- Handwriting-recognition technology has applications in postal automation, cheque processing, document digitization, and reading aids for visually handicapped people..Source: §1 · p. 1
- Scripts with larger vocabularies often have limited training data, challenging word-spotting and recognition research..Source: §1 · p. 1
- Generic image augmentations such as translations, flips, rotations, and noise often fail to capture handwriting’s complex shapes and writing-style variation.The authors argue that networks can adapt to these policies without learning robust invariances to uncommon deformations.Source: §1 · p. 1
- Word recognition transcribes a word image, whereas word spotting locates occurrences of a queried keyword in a collection of word images.Prior approaches include cross-lingual character mapping, whose feasibility depends on script similarity, and GAN-based one-shot augmentation.Source: §1 · p. 2
- The Adversarial Feature Deformation Module (AFDM) augments word-recognition and spotting frameworks with adversarial spatial transformations in feature space.The authors report improved performance and better generalization to real-world handwritten data.Source: §1 · p. 2
2. Handwritten Word Retrieval Models
The paper builds its word-retrieval meta-framework on CRNN for recognition and PHOCNet for spotting. It describes how each baseline processes handwritten word images and notes that the augmentation module can extend to other backbone architectures.
2. Handwritten Word Retrieval Models
- The meta-framework adds adversarial augmentation to CRNN for recognition and PHOCNet for spotting, and can also incorporate ResNet-like architectures.The original frameworks use VGG-like architectures.Source: §2 · p. 2
- CRNN maps convolutional features to a recurrent sequence model that predicts per-frame labels, then uses a transcription layer to produce the word label sequence.Its CTC-based design handles arbitrary-length word sequences without character segmentation.Source: §2 · p. 2
- PHOCNet represents word images through multi-level character and n-gram histograms, then compares predicted word attributes with query representations for spotting.It uses spatial pyramid pooling for variable-size inputs and sigmoid outputs for multi-label prediction.Source: §2 · p. 2
3. Related Works
Prior handwriting-retrieval work combines textual or attribute embeddings, image preprocessing, and data augmentation. Other lines of work mine hard examples or use generative and adversarial models to address data scarcity and uncommon variations.
3. Related Works
- Word-spotting approaches have represented words with textual embeddings, n-gram profiles, PHOC attributes, or region-proposal features.Sequence recognition work also uses CTC-trained recurrent models and spatial attention mechanisms [38] [46].Source: §3 · pp. 2–3
- Many handwriting-recognition and spotting methods preprocess images to extend training data, including work on large datasets and Chinese character recognition.Online hard example mining targets fewer difficult examples [22] [34] [43].Source: §3 · p. 3
- GAN-based methods generate synthetic data, including class-specific samples from a single example in limited-data settings [2] [8] [25] [47]..Source: §3 · p. 3
- Related adversarial work generates feature-space occlusions and spatial deformations to make detectors adapt to rare input variations [44].Other adversarial methods address class imbalance in visual tracking and object detection [36].Source: §3 · p. 3
4. Proposed Methodology
The method adds AFDM to task networks to deform learned features and target difficult irregularities. It combines spatial sampling with adversarial optimization, including separate submap warps to prevent trivial deformation.
4.1. Overview
- AFDM inserts an adversarial deformation module between intermediate task-network layers, learning feature warps intended to adapt models to difficult examples and uncommon irregularities.The framework is described for both word recognition and word spotting.Source: §4.1 · p. 3
4.2. Adversarial Feature Deformation Module
- AFDM predicts transformation parameters, maps a sampling grid, and bilinearly samples the feature map; TPS provides flexible, differentiable elastic warps.Figure 1 depicts the Localisation Network, Grid Generator, and Sampler deforming the complete feature map.Source: §4.2 · pp. 3–4, Figure 1 · p. 4
4.3. Adversarial Learning
- To discourage trivial warps, AFDM divides the feature map into k submaps, deforms each separately, then concatenates them into a same-sized feature map.The paper specifies that k is much smaller than the number of channels C.Source: §4.3 · p. 5
- During training, the localisation network maximizes task loss while the task network minimizes it; at inference, the task network runs without AFDM.The task network predicts either word transcriptions or PHOC labels, depending on the task.Source: §4.3 · p. 5
5. Experimentation Details
The experiments evaluate AFDM across Latin and Indic handwriting datasets, compare it with image- and feature-level augmentation baselines, and examine training-data size and design choices. The reported comparisons favor feature-space adversarial augmentation, especially in low-data settings and on the Indic datasets.
5.1. Datasets
- Evaluation spans the IAM and RIMES Latin datasets and the IndBAN and IndDEV Indic datasets, which contain 17,091 Bangla and 16,128 Devanagari words, respectively.IAM, IndBAN, and IndDEV use their supplied train-validation-test partitions; RIMES follows the ICDAR 2011 partition.Source: §5.1 · pp. 5–6
5.2. Implementation Details
- Training warms the task network for 10K iterations, initializes AFDM alone for 500 iterations, then alternates both for 100K while deforming half of each batch.The module is inserted after conv4 1 in both PHOCNet and CRNN.Source: §5.2 · pp. 6–7
5.3. Baseline Methods
- The baselines compare image-level augmentation, Latin-to-Indic transfer learning, image-space TPS deformation, and affine adversarial warping.B1 expands the image-level augmentation training set to about 500K word samples; B2 freezes part of a Latin-pretrained network before Indic fine-tuning.Source: §5.3 · p. 7
5.4. Performance on HWR and HWS
- AFDM significantly outperforms image-level augmentation, while feature-space adversarial deformation outperforms image-space deformation and TPS outperforms affine warping.Transfer learning improves both recognition and spotting performance and speeds training; the reported gains from adversarial augmentation are greater on IndBAN and IndDEV.Source: §5.4 · p. 7
5.5. Ablation Study
- On IAM, AFDM gains are largest with less training data, mid-to-deep insertion is more stable, and four sub-map divisions perform best.The study varies training-set size across eight instances from 10K to 80K; insertion between shallower layers leads to divergence, while very deep insertion reduces performance.Source: Figure 2 · p. 7, §5.5 · p. 8
- 27.64% WER and 82.67 mAP-QbS versus 40.22% WER and 71.31 mAP-QbS on hard test words after training on easy samples.The setup uses the top 70% of words as easy training samples and the remaining 30% as hard samples.Source: §5.5 · p. 8
6. Conclusion
The AFDM flexibly extends word-spotting and recognition frameworks, using adversarial TPS warping in feature space to address sparse training data. The authors conclude that it helps incorporate rare unseen variations and generalize in low-data settings.
6. Conclusion
- AFDM can be flexibly added to frameworks for both handwritten word spotting and recognition, supporting generalization in low-data settings.Source: §6 · p. 8
- Adversarial TPS warping of intermediate features is presented as a scalable alternative to handcrafted image-space augmentation for sparse datasets.Source: §6 · p. 8
- TPS flexibility and adversarial parameterization are described as incorporating rare unseen variations and outperforming deformation policies that frameworks can easily overfit to.Source: §6 · p. 8