Training OCR Models for Tibetan Pecha: Challenges and Solutions
Introduction
Training OCR models for Tibetan pecha comes with a unique challenge. Unlike Latin-based texts, Tibetan pecha manuscripts feature intricate calligraphic styles, varying glyph spacing, and uncommon ligatures that can make typical OCR models obsolete. On top of that, there’s a scarcity of annotated Tibetan data, which complicates the process even further.
Many people use Transkribus, a tool for historical manuscripts, to handle Tibetan OCR. However, Transkribus was designed for European texts, so adapting it to Tibetan pecha requires a lot of manual work. On the other hand, a synthetic data approach, which uses custom font generation, can provide a faster and more scalable alternative by creating data tailored specifically to the stylistic complexities of Tibetan pecha. In this blog, we’ll compare the two approaches, looking at their efficiency, flexibility, and accuracy, and show why a custom font might just be the more accessible and adaptable solution for Tibetan OCR.
1. Training a Custom Model for Tibetan Pecha in Transkribus
While Transkribus can be used for Tibetan text, getting it to work well requires a highly manual workflow, especially for scripts from Pecha, which has unique stylistic elements. Here’s a closer look at the process and some of the challenges involved:
Data Collection: The first step in Transkribus custom model training is gathering high-quality scans of Tibetan pecha manuscripts. There are several publishers of Tibetan pecha, so there’s a lot of stylistic diversity—not only in the shape of the glyphs but also in layout, style, and spacing. Training a single model is crucial but challenging, especially given the stylistic variation of glyphs.
Segmentation and Annotation: Each page of a Pecha manuscript has to be segmented into lines, words, and characters so the model can learn the structure of the text. Tibetan scripts often have complex ligatures and overlapping characters that models struggle with if they aren’t properly segmented, so this step requires precision.
Transcription: Once segmented, each part needs to be manually transcribed into Tibetan Unicode. For training in Transkribus to work well, you need a lot of text data—often thousands of paired image-text lines. This transcription process is labour-intensive and requires deep knowledge of the Tibetan script to ensure an accurate representation of each glyph and ligature.
Training Process: With enough annotated data in place, Transkribus uses its HTR+ (Handwritten Text Recognition) system to train the model. However, because Transkribus is primarily designed for Western scripts, adapting it to Tibetan script often requires more data and careful handling to capture the script’s complexity. Even after training, you might still need to manually correct the output to improve accuracy.
Iterative Fine-Tuning: To enhance the model’s performance, more pecha manuscripts with stylistic variations need to be annotated and added to the dataset. Because Transkribus doesn’t natively support Tibetan calligraphic styles, this fine-tuning process is usually slow, costly, and labour-intensive, but it’s necessary to improve accuracy.
Fig.1. Transkribus custom Tibetan Model not able to recognize text from Derge Kangyur
2. Font Generation Process for Synthetic Tibetan Pecha Data
A different approach involves using synthetic data, generated with a custom Tibetan font that mirrors the traditional pecha style. This method is faster, less labour-intensive, and leverages glyphs from real manuscripts to create a digital font that replicates the look and feel of Pecha. Here’s how it works:
Font Creation from Glyphs: The process begins with extracting individual glyphs from Tibetan pecha manuscripts, which form the basis of a custom digital font. This font is designed to capture the stylistic details unique to Pecha—such as shape, spacing, and line thickness—allowing the synthetic data to closely resemble actual Pecha text.
Glyph Extracting Process
Synthetic Data Generation: With the font in place, it’s easy to generate Tibetan pecha-style text samples programmatically. Thousands of line images and text pairs can be created quickly, covering a variety of styles, sizes, and arrangements. Programmatic augmentation, like altering spacing, rotation, and size, introduces variation to help the model recognize real-world pecha documents more accurately.
Font Generation Process
OCR Model Training with Synthetic Data: The synthetic data, already labelled with Tibetan characters, can go directly into training the OCR model. This means no manual segmentation or transcription is needed, as each character is automatically labelled when generated. The model learns to recognize Tibetan glyph patterns and styles without the heavy manual workload required by Transkribus.
Fine-tuning with Real Pecha Scans (if needed): While synthetic font data provides a strong foundation, fine-tuning with real Pecha scans can improve accuracy for unique stylistic elements not captured in the font. That said because the synthetic font covers a wide range of stylistic details, the need for extensive fine-tuning is usually minimal.
Fig.2. Generated Test font from Derge Kangyur without augmentation
Font File
Why the Font Generation Process is Better
Reduced Dependency on Manual Labor: Using a custom font to generate synthetic data sidesteps the intensive transcription and segmentation work required by Transkribus. This makes it possible to build large training datasets quickly and at a lower cost—ideal for resource-constrained projects or institutions.
Higher Adaptability to Tibetan Script: Unlike Transkribus, which was created for Western languages, the font-based approach directly addresses the unique calligraphy and cultural nuances of Tibetan pecha. By generating data tailored to Tibetan script, this method enhances cultural accuracy in the OCR results.
Scalability for Robust OCR Models: With synthetic data, it’s easy to scale up the dataset to include thousands of labelled samples with diverse styles and features. This variation helps the OCR model learn a broad range of patterns, preparing it to handle real-world Tibetan documents, which can vary widely in script style.
More Accurate OCR Results: The synthetic font closely mirrors traditional pecha styles, allowing OCR models to achieve a level of accuracy and cultural fidelity that’s hard to reach with Transkribus alone. Font-based training can produce highly realistic representations of Pecha text, requiring less fine-tuning and correction.