Handwritten Word Recognition for Low-Resource Languages: A CRNN-CTC Framework for Kirundi
TL;DR
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.
Abstract
Handwritten Text Recognition (HTR) has experienced remarkable progress with the development of deep learning techniques. However, most existing studies focus on high-resource languages for which large annotated datasets are readily available. In contrast, low-resource languages remain largely underrepresented in handwriting recognition research due to the scarcity of handwritten corpora, linguistic resources, and benchmark datasets. This paper presents a handwritten word recognition framework for Kirundi, a low-resource Bantu language spoken primarily in Burundi. The proposed system employs a Convolutional Recurrent Neural Network (CRNN) combined with Connectionist Temporal Classification (CTC) for end-to-end sequence recognition without explicit character segmentation. To address severe data scarcity, a small handwritten Kirundi dataset consisting of manually collected word samples was constructed and annotated. Data augmentation techniques, including rotation, translation, Gaussian noise, Gaussian blur, and elastic distortion, were applied to increase sample diversity. In addition, synthetic handwritten-style data were generated to further expand the training set. Three experimental configurations were investigated: real handwritten data only, real data with augmentation, and real data combined with augmentation and synthetic handwritten-style images. Experimental results demonstrate that synthetic data generation improved recognition performance and reduced Character Error Rate (CER) from 0.8354 to 0.7560, corresponding to an approximate relative improvement of 9.5%. 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. The study establishes an initial benchmark for Kirundi handwritten word recognition and highlights the potential of synthetic data generation for low-resource handwriting recognition tasks.