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State-of-the-art offline Optical Character Recognition (OCR) frameworksperform poorly on semi-structured handwritten domain-specific documents due totheir inability to localize and label form fields with domain-specificsemantics. Existing techniques for semi-structured document analysis haveprimarily used datasets comprising invoices, purchase orders, receipts, andidentity-card documents for benchmarking. In this work, we build the firstsemi-structured document analysis dataset in the legal domain by collecting alarge number of First Information Report (FIR) documents from several policestations in India. This dataset, which we call the FIR dataset, is morechallenging than most existing document analysis datasets, since it combines awide variety of handwritten text with printed text. We also propose anend-to-end framework for offline processing of handwritten semi-structureddocuments, and benchmark it on our novel FIR dataset. Our framework usedEncoder-Decoder architecture for localizing and labelling the form fields andfor recognizing the handwritten content. The encoder consists of Faster-RCNNand Vision Transformers. Further the Transformer-based decoder architecture istrained with a domain-specific tokenizer. We also propose a post-correctionmethod to handle recognition errors pertaining to the domain-specific terms.Our proposed framework achieves state-of-the-art results on the FIR datasetoutperforming several existing models

TransDocAnalyser: A Framework for Offline Semi-structured Handwritten Document Analysis in the Legal Domain