InPage-to-Unicode Converter

Updated 10 January 2026

InPage-to-Unicode Converter is a software tool that transforms legacy InPage documents into standard Unicode text, essential for Kashmiri NLP.
It employs multi-stage processing including binary parsing, character mapping, diacritic reordering, and Unicode normalization to address encoding complexities.
This conversion pipeline underpins corpus construction, machine translation, and cross-script normalization, driving advancements in low-resource language technologies.

An InPage-to-Unicode converter is a specialized software component designed to transform documents written in the proprietary InPage desktop publishing format into standard Unicode text streams. The majority of Kashmiri literary and journalistic material composed since the 1990s exists solely in InPage format, which encodes Perso-Arabic script via non-standard, byte-level codepoints. As a result, a robust InPage-to-Unicode pipeline is a prerequisite for modern NLP, corpus creation, and LLM pretraining in Kashmiri and related low-resource scripts (Malik, 3 Jan 2026).

1. Technical Architecture of InPage-to-Unicode Conversion

The converter operates by reversing InPage’s proprietary, byte-oriented encoding to reconstruct a canonical Unicode sequence for each character, including the correct ordering of Kashmiri base letters, ligature expansions, and diacritics. The pipeline consists of the following algorithmic stages:

Parsing InPage binary streams: Extracting records from the InPage file using documented structure or reverse-engineering.
Mapping InPage codepoints to Unicode: Utilizing an explicit character mapping table. A representative mapping includes:

| InPage Code | Description | Unicode Sequence | |-------------|------------------------|----------------------| | 0xA1 | Kashmiri ‘vaaw’ | U+06CB | | 0xA2 | Alif + superscript | U+0627 U+0654 | | 0xB0 | Zabar (short ‘a’) | U+063E | | 0xB1 | Zer | U+0650 | | 0xB2 | Pesh (long ‘o’) | U+064F |

Combining diacritic reconstruction: After mapping, diacritics are reordered according to Unicode combining class as: $[\text{base}] \rightarrow [\text{U+0654 (hamza)}] \rightarrow [\text{vowel marks}] \rightarrow [\text{other diacritics}]$ .
Unicode normalization: The linear codepoint sequence is normalized to NFC (Normalization Form Canonical Composition).

A high-level pseudocode abstraction:

def convert_InPage_document(binary_stream):
    records = parse_InPage_records(binary_stream)
    unicode_output = []
    for rec in records:
        base_id = map_inpage_base[rec.codepoint]
        diacritic_ids = [map_inpage_diacritic[flag] for flag in rec.diacritic_flags]
        u_sequence = reconstruct_unicode_sequence(base_id, diacritic_ids)
        unicode_output.append(u_sequence)
    text = "".join(unicode_output)
    return unicode_normalize_NFC(text)

(Malik, 3 Jan 2026)

2. Preprocessing and Corpus Cleaning Workflow

Converted Unicode text requires further normalization and filtering to ensure high corpus quality:

English contamination removal: Employing sentence-level n-gram language detection (e.g., fastText), sentences with English-confidence > 0.80 are dropped. If Latin-script substrings exceed 30% of tokens, those spans are removed.
Canonical character normalization: Unicode NFC is applied globally, and visually identical script variants are collapsed using a lookup table.
Whitespace and paragraph formatting: Runs of whitespace are collapsed, and paragraphs are separated by double newlines.
Quality control checks: Lines containing “�” or InPage-specific placeholders are flagged; if over 5% of a line is suspect, the line is dropped or reviewed. Per-document character error rate (CER) is checked against a human-validated reference; CER > 2% triggers document rejection.

Pipeline pseudocode:

def preprocess(text):
    sents = sentence_tokenize(text)
    clean_sents = []
    for s in sents:
        if langid(s).confidence("en") < 0.80:
            s2 = regex.sub(r"[A-Za-z]+([-'][A-Za-z]+)*","",s)
            clean_sents.append(s2)
    joined = " ".join(clean_sents)
    normed = unicode_normalize_NFC(joined)
    ws_fixed = regex.sub(r"\s+"," ",normed).strip()
    paras = split_on_double_newline(ws_fixed)
    final = "\n\n".join(p.strip() for p in paras)
    if document_CER(final) > 0.02:
        reject_document()
    return final

(Malik, 3 Jan 2026)

3. Linguistic and Computational Challenges

Converting InPage to Unicode in the Kashmiri context poses unique challenges:

Non-reversible encodings: InPage files often collapse multiple distinct Unicode sequences into a single codepoint for ligatures or contextual alternates. Disambiguation requires heuristic or font-specific expansion rules.
Complex diacritic combinations: The Kashmiri Perso-Arabic script exhibits dense diacritic stacking (e.g., hamza plus multiple vowels), necessitating strict Unicode combining order.
Line-breaking and justification artifacts: InPage layout codes can introduce extraneous whitespace or pseudo-glyphs, which must be filtered from the logical text output.
Quality validation: Ensuring OCR or transcription equivalence after conversion is non-trivial due to rendering differences between legacy fonts and Unicode-aware processors.

A plausible implication is that InPage-to-Unicode conversion quality directly constrains the upper bound of accuracy for downstream LLM pretraining and machine translation.

4. Applications in Kashmiri NLP and Corpus Construction

The primary application of a robust InPage-to-Unicode converter is the unlocking of otherwise inaccessible Kashmiri text, enabling the creation of large-scale corpora for:

LLM pretraining: As demonstrated in the KS-LIT-3M corpus, 3.1 million words of Kashmiri text were extracted from InPage archives and released as a continuous Unicode stream for training causal LLMs. This corpus comprises 131,607 unique words and 16.4 million characters, with coverage across literary, journalistic, academic, and religious genres (Malik, 3 Jan 2026).
Parallel and monolingual corpora for machine translation: The BhashaVerse ecosystem leverages script normalization and conversion pipelines to align Kashmiri in both Perso-Arabic and Devanagari with English, Hindi, and other Indian languages, yielding paired corpora on the order of tens of millions of sentence pairs (Mujadia et al., 2024).
OCR evaluation and text digitization: The output of InPage-to-Unicode workflows can be paired with contemporary OCR datasets (e.g., 600K-KS-OCR) for validation and benchmarking, as well as scaling annotation efforts for low-resource scripts (Malik, 3 Jan 2026).

5. Interoperability, Script Normalization, and Future Directions

In the broader multilingual NLP pipeline, conversion from InPage-encoded Kashmiri to Unicode is frequently combined with script normalization procedures—e.g., mapping Kashmiri Perso-Arabic to Devanagari for cross-script MT and vocabulary unification. BhashaVerse defines bijective character mapping functions between Kashmiri scripts and reports ≥97% round-trip fidelity in script conversion, enabling subword vocabulary sharing across scripts (Mujadia et al., 2024).

Recommended areas of technical extension include:

Expansion of mapping tables to cover regional ligatures and rare glyphs.
Automated post-editing pipelines to correct residual conversion artifacts based on human-annotated post-edits.
Integration with OCR and handwriting recognition models for end-to-end digitization workflows.

Continued refinement and open release of InPage-to-Unicode converters and conversion specifications are essential for scalable, reproducible research in Kashmiri language technology, directly enabling advancements in LLM pretraining, translation, and digital preservation (Malik, 3 Jan 2026, Mujadia et al., 2024).

6. Significance in the South Asian Language Technology Landscape

The introduction and implementation of InPage-to-Unicode conversion functionality have transformed the feasibility of Kashmiri NLP research. Prior to the release of conversion pipelines and derived resources like KS-LIT-3M, the field lacked sufficient digital text for modern model training: no tokenizers, POS taggers, MT systems, or speech models for Kashmiri were reported as of late 2024 (Gupta, 2024). By enabling both corpus construction and script normalization across multiple scripts, InPage-to-Unicode conversion underpins the emergence of baseline systems and evaluation metrics throughout the wider South Asian language ecosystem, serving as an essential infrastructure for future tool and benchmark development.