| <!DOCTYPE html>
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| <html lang="en">
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| <head>
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| <meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
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| <meta name="generator" content="AsciiDoc 10.2.0">
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| <title>COMBINE_TESSDATA(1)</title>
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| <style type="text/css">
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| body {
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| font-family: Georgia,serif;
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| }
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| h1, h2, h3, h4, h5, h6,
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| div.title, caption.title,
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| thead, p.table.header,
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| #toctitle,
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| #author, #revnumber, #revdate, #revremark,
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| #footer {
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| font-family: Arial,Helvetica,sans-serif;
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| }
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|
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| body {
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| margin: 1em 5% 1em 5%;
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| }
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| a {
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| color: blue;
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| text-decoration: underline;
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| }
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| a:visited {
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| color: fuchsia;
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| }
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|
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| em {
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| font-style: italic;
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| color: navy;
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| }
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|
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| strong {
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| font-weight: bold;
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| color: #083194;
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| }
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|
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| h1, h2, h3, h4, h5, h6 {
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| color: #527bbd;
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| margin-top: 1.2em;
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| margin-bottom: 0.5em;
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| line-height: 1.3;
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| }
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|
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| h1, h2, h3 {
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| border-bottom: 2px solid silver;
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| }
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| h2 {
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| padding-top: 0.5em;
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| }
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| h3 {
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| float: left;
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| }
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| h3 + * {
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| clear: left;
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| }
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| h5 {
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| font-size: 1.0em;
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| }
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|
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| div.sectionbody {
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| margin-left: 0;
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| }
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|
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| hr {
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| border: 1px solid silver;
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| }
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|
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| p {
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| margin-top: 0.5em;
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| margin-bottom: 0.5em;
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| }
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|
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| ul, ol, li > p {
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| margin-top: 0;
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| }
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| ul > li { color: #aaa; }
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| ul > li > * { color: black; }
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|
|
| .monospaced, code, pre {
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| font-family: "Courier New", Courier, monospace;
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| font-size: inherit;
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| color: navy;
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| padding: 0;
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| margin: 0;
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| }
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| pre {
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| white-space: pre-wrap;
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| }
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|
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| #author {
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| color: #527bbd;
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| font-weight: bold;
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| font-size: 1.1em;
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| }
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| #email {
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| }
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| #revnumber, #revdate, #revremark {
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| }
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|
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| #footer {
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| font-size: small;
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| border-top: 2px solid silver;
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| padding-top: 0.5em;
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| margin-top: 4.0em;
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| }
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| #footer-text {
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| float: left;
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| padding-bottom: 0.5em;
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| }
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| #footer-badges {
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| float: right;
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| padding-bottom: 0.5em;
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| }
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|
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| #preamble {
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| margin-top: 1.5em;
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| margin-bottom: 1.5em;
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| }
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| div.imageblock, div.exampleblock, div.verseblock,
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| div.quoteblock, div.literalblock, div.listingblock, div.sidebarblock,
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| div.admonitionblock {
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| margin-top: 1.0em;
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| margin-bottom: 1.5em;
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| }
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| div.admonitionblock {
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| margin-top: 2.0em;
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| margin-bottom: 2.0em;
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| margin-right: 10%;
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| color: #606060;
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| }
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| div.content {
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| padding: 0;
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| }
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|
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| div.title, caption.title {
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| color: #527bbd;
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| font-weight: bold;
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| text-align: left;
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| margin-top: 1.0em;
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| margin-bottom: 0.5em;
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| }
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| div.title + * {
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| margin-top: 0;
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| }
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|
|
| td div.title:first-child {
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| margin-top: 0.0em;
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| }
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| div.content div.title:first-child {
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| margin-top: 0.0em;
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| }
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| div.content + div.title {
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| margin-top: 0.0em;
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| }
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|
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| div.sidebarblock > div.content {
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| background: #ffffee;
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| border: 1px solid #dddddd;
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| border-left: 4px solid #f0f0f0;
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| padding: 0.5em;
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| }
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|
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| div.listingblock > div.content {
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| border: 1px solid #dddddd;
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| border-left: 5px solid #f0f0f0;
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| background: #f8f8f8;
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| padding: 0.5em;
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| }
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| div.quoteblock, div.verseblock {
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| padding-left: 1.0em;
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| margin-left: 1.0em;
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| margin-right: 10%;
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| border-left: 5px solid #f0f0f0;
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| color: #888;
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| }
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|
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| div.quoteblock > div.attribution {
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| padding-top: 0.5em;
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| text-align: right;
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| }
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|
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| div.verseblock > pre.content {
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| font-family: inherit;
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| font-size: inherit;
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| }
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| div.verseblock > div.attribution {
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| padding-top: 0.75em;
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| text-align: left;
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| }
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| div.verseblock + div.attribution {
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| text-align: left;
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| }
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|
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| div.admonitionblock .icon {
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| vertical-align: top;
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| font-size: 1.1em;
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| font-weight: bold;
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| text-decoration: underline;
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| color: #527bbd;
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| padding-right: 0.5em;
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| }
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| div.admonitionblock td.content {
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| padding-left: 0.5em;
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| border-left: 3px solid #dddddd;
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| }
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|
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| div.exampleblock > div.content {
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| border-left: 3px solid #dddddd;
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| padding-left: 0.5em;
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| }
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|
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| div.imageblock div.content { padding-left: 0; }
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| span.image img { border-style: none; vertical-align: text-bottom; }
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| a.image:visited { color: white; }
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|
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| dl {
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| margin-top: 0.8em;
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| margin-bottom: 0.8em;
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| }
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| dt {
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| margin-top: 0.5em;
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| margin-bottom: 0;
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| font-style: normal;
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| color: navy;
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| }
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| dd > *:first-child {
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| margin-top: 0.1em;
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| }
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|
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| ul, ol {
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| list-style-position: outside;
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| }
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| ol.arabic {
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| list-style-type: decimal;
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| }
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| ol.loweralpha {
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| list-style-type: lower-alpha;
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| }
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| ol.upperalpha {
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| list-style-type: upper-alpha;
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| }
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| ol.lowerroman {
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| list-style-type: lower-roman;
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| }
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| ol.upperroman {
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| list-style-type: upper-roman;
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| }
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| div.compact ul, div.compact ol,
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| div.compact p, div.compact p,
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| div.compact div, div.compact div {
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| margin-top: 0.1em;
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| margin-bottom: 0.1em;
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| }
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|
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| tfoot {
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| font-weight: bold;
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| }
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| td > div.verse {
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| white-space: pre;
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| }
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|
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| div.hdlist {
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| margin-top: 0.8em;
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| margin-bottom: 0.8em;
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| }
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| div.hdlist tr {
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| padding-bottom: 15px;
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| }
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| dt.hdlist1.strong, td.hdlist1.strong {
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| font-weight: bold;
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| }
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| td.hdlist1 {
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| vertical-align: top;
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| font-style: normal;
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| padding-right: 0.8em;
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| color: navy;
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| }
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| td.hdlist2 {
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| vertical-align: top;
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| }
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| div.hdlist.compact tr {
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| margin: 0;
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| padding-bottom: 0;
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| }
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|
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| .comment {
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| background: yellow;
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| }
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|
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| .footnote, .footnoteref {
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| font-size: 0.8em;
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| }
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|
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| span.footnote, span.footnoteref {
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| vertical-align: super;
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| }
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| #footnotes {
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| margin: 20px 0 20px 0;
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| padding: 7px 0 0 0;
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| }
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|
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| #footnotes div.footnote {
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| margin: 0 0 5px 0;
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| }
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|
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| #footnotes hr {
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| border: none;
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| border-top: 1px solid silver;
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| height: 1px;
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| text-align: left;
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| margin-left: 0;
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| width: 20%;
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| min-width: 100px;
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| }
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|
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| div.colist td {
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| padding-right: 0.5em;
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| padding-bottom: 0.3em;
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| vertical-align: top;
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| }
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| div.colist td img {
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| margin-top: 0.3em;
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| }
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| @media print {
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| #footer-badges { display: none; }
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| }
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| #toc {
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| margin-bottom: 2.5em;
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| }
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|
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| #toctitle {
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| color: #527bbd;
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| font-size: 1.1em;
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| font-weight: bold;
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| margin-top: 1.0em;
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| margin-bottom: 0.1em;
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| }
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|
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| div.toclevel0, div.toclevel1, div.toclevel2, div.toclevel3, div.toclevel4 {
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| margin-top: 0;
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| margin-bottom: 0;
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| }
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| div.toclevel2 {
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| margin-left: 2em;
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| font-size: 0.9em;
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| }
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| div.toclevel3 {
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| margin-left: 4em;
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| font-size: 0.9em;
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| }
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| div.toclevel4 {
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| margin-left: 6em;
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| font-size: 0.9em;
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| }
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|
|
| span.aqua { color: aqua; }
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| span.black { color: black; }
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| span.blue { color: blue; }
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| span.fuchsia { color: fuchsia; }
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| span.gray { color: gray; }
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| span.green { color: green; }
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| span.lime { color: lime; }
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| span.maroon { color: maroon; }
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| span.navy { color: navy; }
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| span.olive { color: olive; }
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| span.purple { color: purple; }
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| span.red { color: red; }
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| span.silver { color: silver; }
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| span.teal { color: teal; }
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| span.white { color: white; }
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| span.yellow { color: yellow; }
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|
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| span.aqua-background { background: aqua; }
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| span.black-background { background: black; }
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| span.blue-background { background: blue; }
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| span.fuchsia-background { background: fuchsia; }
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| span.gray-background { background: gray; }
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| span.green-background { background: green; }
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| span.lime-background { background: lime; }
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| span.maroon-background { background: maroon; }
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| span.navy-background { background: navy; }
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| span.olive-background { background: olive; }
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| span.purple-background { background: purple; }
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| span.red-background { background: red; }
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| span.silver-background { background: silver; }
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| span.teal-background { background: teal; }
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| span.white-background { background: white; }
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| span.yellow-background { background: yellow; }
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|
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| span.big { font-size: 2em; }
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| span.small { font-size: 0.6em; }
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|
|
| span.underline { text-decoration: underline; }
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| span.overline { text-decoration: overline; }
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| span.line-through { text-decoration: line-through; }
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|
|
| div.unbreakable { page-break-inside: avoid; }
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|
|
|
| |
| |
| |
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| div.tableblock {
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| margin-top: 1.0em;
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| margin-bottom: 1.5em;
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| }
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| div.tableblock > table {
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| border: 3px solid #527bbd;
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| }
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| thead, p.table.header {
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| font-weight: bold;
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| color: #527bbd;
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| }
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| p.table {
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| margin-top: 0;
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| }
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|
|
| div.tableblock > table[frame="void"] {
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| border-style: none;
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| }
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| div.tableblock > table[frame="hsides"] {
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| border-left-style: none;
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| border-right-style: none;
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| }
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| div.tableblock > table[frame="vsides"] {
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| border-top-style: none;
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| border-bottom-style: none;
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| }
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|
|
|
|
| |
| |
| |
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|
|
|
| table.tableblock {
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| margin-top: 1.0em;
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| margin-bottom: 1.5em;
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| }
|
| thead, p.tableblock.header {
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| font-weight: bold;
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| color: #527bbd;
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| }
|
| p.tableblock {
|
| margin-top: 0;
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| }
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| table.tableblock {
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| border-width: 3px;
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| border-spacing: 0px;
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| border-style: solid;
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| border-color: #527bbd;
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| border-collapse: collapse;
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| }
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| th.tableblock, td.tableblock {
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| border-width: 1px;
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| padding: 4px;
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| border-style: solid;
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| border-color: #527bbd;
|
| }
|
|
|
| table.tableblock.frame-topbot {
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| border-left-style: hidden;
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| border-right-style: hidden;
|
| }
|
| table.tableblock.frame-sides {
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| border-top-style: hidden;
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| border-bottom-style: hidden;
|
| }
|
| table.tableblock.frame-none {
|
| border-style: hidden;
|
| }
|
|
|
| th.tableblock.halign-left, td.tableblock.halign-left {
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| text-align: left;
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| }
|
| th.tableblock.halign-center, td.tableblock.halign-center {
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| text-align: center;
|
| }
|
| th.tableblock.halign-right, td.tableblock.halign-right {
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| text-align: right;
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| }
|
|
|
| th.tableblock.valign-top, td.tableblock.valign-top {
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| vertical-align: top;
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| }
|
| th.tableblock.valign-middle, td.tableblock.valign-middle {
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| vertical-align: middle;
|
| }
|
| th.tableblock.valign-bottom, td.tableblock.valign-bottom {
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| vertical-align: bottom;
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| }
|
|
|
|
|
| |
| |
| |
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|
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| body.manpage h1 {
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| padding-top: 0.5em;
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| padding-bottom: 0.5em;
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| border-top: 2px solid silver;
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| border-bottom: 2px solid silver;
|
| }
|
| body.manpage h2 {
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| border-style: none;
|
| }
|
| body.manpage div.sectionbody {
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| margin-left: 3em;
|
| }
|
|
|
| @media print {
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| body.manpage div#toc { display: none; }
|
| }
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|
|
|
|
| </style>
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| <script type="text/javascript">
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|
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| var asciidoc = {
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| toc: function (toclevels) {
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|
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| function getText(el) {
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| var text = "";
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| for (var i = el.firstChild; i != null; i = i.nextSibling) {
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| if (i.nodeType == 3 )
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| text += i.data;
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| else if (i.firstChild != null)
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| text += getText(i);
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| }
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| return text;
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| }
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|
|
| function TocEntry(el, text, toclevel) {
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| this.element = el;
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| this.text = text;
|
| this.toclevel = toclevel;
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| }
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|
|
| function tocEntries(el, toclevels) {
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| var result = new Array;
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| var re = new RegExp('[hH]([1-'+(toclevels+1)+'])');
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|
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|
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| var iterate = function (el) {
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| for (var i = el.firstChild; i != null; i = i.nextSibling) {
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| if (i.nodeType == 1 ) {
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| var mo = re.exec(i.tagName);
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| if (mo && (i.getAttribute("class") || i.getAttribute("className")) != "float") {
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| result[result.length] = new TocEntry(i, getText(i), mo[1]-1);
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| }
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| iterate(i);
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| }
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| }
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| }
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| iterate(el);
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| return result;
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| }
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|
|
| var toc = document.getElementById("toc");
|
| if (!toc) {
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| return;
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| }
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|
|
| var tocEntriesToRemove = [];
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| var i;
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| for (i = 0; i < toc.childNodes.length; i++) {
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| var entry = toc.childNodes[i];
|
| if (entry.nodeName.toLowerCase() == 'div'
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| && entry.getAttribute("class")
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| && entry.getAttribute("class").match(/^toclevel/))
|
| tocEntriesToRemove.push(entry);
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| }
|
| for (i = 0; i < tocEntriesToRemove.length; i++) {
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| toc.removeChild(tocEntriesToRemove[i]);
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| }
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|
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| var entries = tocEntries(document.getElementById("content"), toclevels);
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| for (var i = 0; i < entries.length; ++i) {
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| var entry = entries[i];
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| if (entry.element.id == "")
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| entry.element.id = "_toc_" + i;
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| var a = document.createElement("a");
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| a.href = "#" + entry.element.id;
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| a.appendChild(document.createTextNode(entry.text));
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| var div = document.createElement("div");
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| div.appendChild(a);
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| div.className = "toclevel" + entry.toclevel;
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| toc.appendChild(div);
|
| }
|
| if (entries.length == 0)
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| toc.parentNode.removeChild(toc);
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| },
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| |
| |
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|
| footnotes: function () {
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|
|
| var i;
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| var noteholder = document.getElementById("footnotes");
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| if (!noteholder) {
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| return;
|
| }
|
| var entriesToRemove = [];
|
| for (i = 0; i < noteholder.childNodes.length; i++) {
|
| var entry = noteholder.childNodes[i];
|
| if (entry.nodeName.toLowerCase() == 'div' && entry.getAttribute("class") == "footnote")
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| entriesToRemove.push(entry);
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| }
|
| for (i = 0; i < entriesToRemove.length; i++) {
|
| noteholder.removeChild(entriesToRemove[i]);
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| }
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|
|
|
|
| var cont = document.getElementById("content");
|
| var spans = cont.getElementsByTagName("span");
|
| var refs = {};
|
| var n = 0;
|
| for (i=0; i<spans.length; i++) {
|
| if (spans[i].className == "footnote") {
|
| n++;
|
| var note = spans[i].getAttribute("data-note");
|
| if (!note) {
|
|
|
|
|
| note = spans[i].innerHTML.match(/\s*\[([\s\S]*)]\s*/)[1];
|
| spans[i].innerHTML =
|
| "[<a id='_footnoteref_" + n + "' href='#_footnote_" + n +
|
| "' title='View footnote' class='footnote'>" + n + "</a>]";
|
| spans[i].setAttribute("data-note", note);
|
| }
|
| noteholder.innerHTML +=
|
| "<div class='footnote' id='_footnote_" + n + "'>" +
|
| "<a href='#_footnoteref_" + n + "' title='Return to text'>" +
|
| n + "</a>. " + note + "</div>";
|
| var id =spans[i].getAttribute("id");
|
| if (id != null) refs["#"+id] = n;
|
| }
|
| }
|
| if (n == 0)
|
| noteholder.parentNode.removeChild(noteholder);
|
| else {
|
|
|
| for (i=0; i<spans.length; i++) {
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| <body class="article">
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| <div id="header">
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| <h1>COMBINE_TESSDATA(1)</h1>
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| </div>
|
| <div id="content">
|
| <div class="sect1">
|
| <h2 id="_name">NAME</h2>
|
| <div class="sectionbody">
|
| <div class="paragraph"><p>combine_tessdata - combine/extract/overwrite/list/compact Tesseract data</p></div>
|
| </div>
|
| </div>
|
| <div class="sect1">
|
| <h2 id="_synopsis">SYNOPSIS</h2>
|
| <div class="sectionbody">
|
| <div class="paragraph"><p><strong>combine_tessdata</strong> [<em>OPTION</em>] <em>FILE</em>…</p></div>
|
| </div>
|
| </div>
|
| <div class="sect1">
|
| <h2 id="_description">DESCRIPTION</h2>
|
| <div class="sectionbody">
|
| <div class="paragraph"><p>combine_tessdata(1) is the main program to combine/extract/overwrite/list/compact
|
| tessdata components in [lang].traineddata files.</p></div>
|
| <div class="paragraph"><p>To combine all the individual tessdata components (unicharset, DAWGs,
|
| classifier templates, ambiguities, language configs) located at, say,
|
| /home/$USER/temp/eng.* run:</p></div>
|
| <div class="literalblock">
|
| <div class="content monospaced">
|
| <pre>combine_tessdata /home/$USER/temp/eng.</pre>
|
| </div></div>
|
| <div class="paragraph"><p>The result will be a combined tessdata file /home/$USER/temp/eng.traineddata</p></div>
|
| <div class="paragraph"><p>Specify option -e if you would like to extract individual components
|
| from a combined traineddata file. For example, to extract language config
|
| file and the unicharset from tessdata/eng.traineddata run:</p></div>
|
| <div class="literalblock">
|
| <div class="content monospaced">
|
| <pre>combine_tessdata -e tessdata/eng.traineddata \
|
| /home/$USER/temp/eng.config /home/$USER/temp/eng.unicharset</pre>
|
| </div></div>
|
| <div class="paragraph"><p>The desired config file and unicharset will be written to
|
| /home/$USER/temp/eng.config /home/$USER/temp/eng.unicharset</p></div>
|
| <div class="paragraph"><p>Specify option -o to overwrite individual components of the given
|
| [lang].traineddata file. For example, to overwrite language config
|
| and unichar ambiguities files in tessdata/eng.traineddata use:</p></div>
|
| <div class="literalblock">
|
| <div class="content monospaced">
|
| <pre>combine_tessdata -o tessdata/eng.traineddata \
|
| /home/$USER/temp/eng.config /home/$USER/temp/eng.unicharambigs</pre>
|
| </div></div>
|
| <div class="paragraph"><p>As a result, tessdata/eng.traineddata will contain the new language config
|
| and unichar ambigs, plus all the original DAWGs, classifier templates, etc.</p></div>
|
| <div class="paragraph"><p>Note: the file names of the files to extract to and to overwrite from should
|
| have the appropriate file suffixes (extensions) indicating their tessdata
|
| component type (.unicharset for the unicharset, .unicharambigs for unichar
|
| ambigs, etc). See k*FileSuffix variable in ccutil/tessdatamanager.h.</p></div>
|
| <div class="paragraph"><p>Specify option -u to unpack all the components to the specified path:</p></div>
|
| <div class="literalblock">
|
| <div class="content monospaced">
|
| <pre>combine_tessdata -u tessdata/eng.traineddata /home/$USER/temp/eng.</pre>
|
| </div></div>
|
| <div class="paragraph"><p>This will create /home/$USER/temp/eng.* files with individual tessdata
|
| components from tessdata/eng.traineddata.</p></div>
|
| </div>
|
| </div>
|
| <div class="sect1">
|
| <h2 id="_options">OPTIONS</h2>
|
| <div class="sectionbody">
|
| <div class="paragraph"><p><strong>-c</strong> <em>.traineddata</em> <em>FILE</em>…:
|
| Compacts the LSTM component in the .traineddata file to int.</p></div>
|
| <div class="paragraph"><p><strong>-d</strong> <em>.traineddata</em> <em>FILE</em>…:
|
| Lists directory of components from the .traineddata file.</p></div>
|
| <div class="paragraph"><p><strong>-e</strong> <em>.traineddata</em> <em>FILE</em>…:
|
| Extracts the specified components from the .traineddata file</p></div>
|
| <div class="paragraph"><p><strong>-l</strong> <em>.traineddata</em> <em>FILE</em>…:
|
| List the network information.</p></div>
|
| <div class="paragraph"><p><strong>-o</strong> <em>.traineddata</em> <em>FILE</em>…:
|
| Overwrites the specified components of the .traineddata file
|
| with those provided on the command line.</p></div>
|
| <div class="paragraph"><p><strong>-u</strong> <em>.traineddata</em> <em>PATHPREFIX</em>
|
| Unpacks the .traineddata using the provided prefix.</p></div>
|
| </div>
|
| </div>
|
| <div class="sect1">
|
| <h2 id="_caveats">CAVEATS</h2>
|
| <div class="sectionbody">
|
| <div class="paragraph"><p><em>Prefix</em> refers to the full file prefix, including period (.)</p></div>
|
| </div>
|
| </div>
|
| <div class="sect1">
|
| <h2 id="_components">COMPONENTS</h2>
|
| <div class="sectionbody">
|
| <div class="paragraph"><p>The components in a Tesseract lang.traineddata file as of
|
| Tesseract 4.0 are briefly described below; For more information on
|
| many of these files, see
|
| <a href="https://tesseract-ocr.github.io/tessdoc/Training-Tesseract.html">https://tesseract-ocr.github.io/tessdoc/Training-Tesseract.html</a>
|
| and
|
| <a href="https://tesseract-ocr.github.io/tessdoc/TrainingTesseract-4.00.html">https://tesseract-ocr.github.io/tessdoc/TrainingTesseract-4.00.html</a></p></div>
|
| <div class="dlist"><dl>
|
| <dt class="hdlist1">
|
| lang.config
|
| </dt>
|
| <dd>
|
| <p>
|
| (Optional) Language-specific overrides to default config variables.
|
| For 4.0 traineddata files, lang.config provides control parameters which
|
| can affect layout analysis, and sub-languages.
|
| </p>
|
| </dd>
|
| <dt class="hdlist1">
|
| lang.unicharset
|
| </dt>
|
| <dd>
|
| <p>
|
| (Required - 3.0x legacy tesseract) The list of symbols that Tesseract recognizes, with properties.
|
| See unicharset(5).
|
| </p>
|
| </dd>
|
| <dt class="hdlist1">
|
| lang.unicharambigs
|
| </dt>
|
| <dd>
|
| <p>
|
| (Optional - 3.0x legacy tesseract) This file contains information on pairs of recognized symbols
|
| which are often confused. For example, <em>rn</em> and <em>m</em>.
|
| </p>
|
| </dd>
|
| <dt class="hdlist1">
|
| lang.inttemp
|
| </dt>
|
| <dd>
|
| <p>
|
| (Required - 3.0x legacy tesseract) Character shape templates for each unichar. Produced by
|
| mftraining(1).
|
| </p>
|
| </dd>
|
| <dt class="hdlist1">
|
| lang.pffmtable
|
| </dt>
|
| <dd>
|
| <p>
|
| (Required - 3.0x legacy tesseract) The number of features expected for each unichar.
|
| Produced by mftraining(1) from <strong>.tr</strong> files.
|
| </p>
|
| </dd>
|
| <dt class="hdlist1">
|
| lang.normproto
|
| </dt>
|
| <dd>
|
| <p>
|
| (Required - 3.0x legacy tesseract) Character normalization prototypes generated by cntraining(1)
|
| from <strong>.tr</strong> files.
|
| </p>
|
| </dd>
|
| <dt class="hdlist1">
|
| lang.punc-dawg
|
| </dt>
|
| <dd>
|
| <p>
|
| (Optional - 3.0x legacy tesseract) A dawg made from punctuation patterns found around words.
|
| The "word" part is replaced by a single space.
|
| </p>
|
| </dd>
|
| <dt class="hdlist1">
|
| lang.word-dawg
|
| </dt>
|
| <dd>
|
| <p>
|
| (Optional - 3.0x legacy tesseract) A dawg made from dictionary words from the language.
|
| </p>
|
| </dd>
|
| <dt class="hdlist1">
|
| lang.number-dawg
|
| </dt>
|
| <dd>
|
| <p>
|
| (Optional - 3.0x legacy tesseract) A dawg made from tokens which originally contained digits.
|
| Each digit is replaced by a space character.
|
| </p>
|
| </dd>
|
| <dt class="hdlist1">
|
| lang.freq-dawg
|
| </dt>
|
| <dd>
|
| <p>
|
| (Optional - 3.0x legacy tesseract) A dawg made from the most frequent words which would have
|
| gone into word-dawg.
|
| </p>
|
| </dd>
|
| <dt class="hdlist1">
|
| lang.fixed-length-dawgs
|
| </dt>
|
| <dd>
|
| <p>
|
| (Optional - 3.0x legacy tesseract) Several dawgs of different fixed lengths — useful for
|
| languages like Chinese.
|
| </p>
|
| </dd>
|
| <dt class="hdlist1">
|
| lang.shapetable
|
| </dt>
|
| <dd>
|
| <p>
|
| (Optional - 3.0x legacy tesseract) When present, a shapetable is an extra layer between the character
|
| classifier and the word recognizer that allows the character classifier to
|
| return a collection of unichar ids and fonts instead of a single unichar-id
|
| and font.
|
| </p>
|
| </dd>
|
| <dt class="hdlist1">
|
| lang.bigram-dawg
|
| </dt>
|
| <dd>
|
| <p>
|
| (Optional - 3.0x legacy tesseract) A dawg of word bigrams where the words are separated by a space
|
| and each digit is replaced by a <em>?</em>.
|
| </p>
|
| </dd>
|
| <dt class="hdlist1">
|
| lang.unambig-dawg
|
| </dt>
|
| <dd>
|
| <p>
|
| (Optional - 3.0x legacy tesseract) .
|
| </p>
|
| </dd>
|
| <dt class="hdlist1">
|
| lang.params-model
|
| </dt>
|
| <dd>
|
| <p>
|
| (Optional - 3.0x legacy tesseract) .
|
| </p>
|
| </dd>
|
| <dt class="hdlist1">
|
| lang.lstm
|
| </dt>
|
| <dd>
|
| <p>
|
| (Required - 4.0 LSTM) Neural net trained recognition model generated by lstmtraining.
|
| </p>
|
| </dd>
|
| <dt class="hdlist1">
|
| lang.lstm-punc-dawg
|
| </dt>
|
| <dd>
|
| <p>
|
| (Optional - 4.0 LSTM) A dawg made from punctuation patterns found around words.
|
| The "word" part is replaced by a single space. Uses lang.lstm-unicharset.
|
| </p>
|
| </dd>
|
| <dt class="hdlist1">
|
| lang.lstm-word-dawg
|
| </dt>
|
| <dd>
|
| <p>
|
| (Optional - 4.0 LSTM) A dawg made from dictionary words from the language.
|
| Uses lang.lstm-unicharset.
|
| </p>
|
| </dd>
|
| <dt class="hdlist1">
|
| lang.lstm-number-dawg
|
| </dt>
|
| <dd>
|
| <p>
|
| (Optional - 4.0 LSTM) A dawg made from tokens which originally contained digits.
|
| Each digit is replaced by a space character. Uses lang.lstm-unicharset.
|
| </p>
|
| </dd>
|
| <dt class="hdlist1">
|
| lang.lstm-unicharset
|
| </dt>
|
| <dd>
|
| <p>
|
| (Required - 4.0 LSTM) The unicode character set that Tesseract recognizes, with properties.
|
| Same unicharset must be used to train the LSTM and build the lstm-*-dawgs files.
|
| </p>
|
| </dd>
|
| <dt class="hdlist1">
|
| lang.lstm-recoder
|
| </dt>
|
| <dd>
|
| <p>
|
| (Required - 4.0 LSTM) Unicharcompress, aka the recoder, which maps the unicharset
|
| further to the codes actually used by the neural network recognizer. This is created as
|
| part of the starter traineddata by combine_lang_model.
|
| </p>
|
| </dd>
|
| <dt class="hdlist1">
|
| lang.version
|
| </dt>
|
| <dd>
|
| <p>
|
| (Optional) Version string for the traineddata file.
|
| First appeared in version 4.0 of Tesseract.
|
| Old version of traineddata files will report Version:Pre-4.0.0.
|
| 4.0 version of traineddata files may include the network spec
|
| used for LSTM training as part of version string.
|
| </p>
|
| </dd>
|
| </dl></div>
|
| </div>
|
| </div>
|
| <div class="sect1">
|
| <h2 id="_history">HISTORY</h2>
|
| <div class="sectionbody">
|
| <div class="paragraph"><p>combine_tessdata(1) first appeared in version 3.00 of Tesseract</p></div>
|
| </div>
|
| </div>
|
| <div class="sect1">
|
| <h2 id="_see_also">SEE ALSO</h2>
|
| <div class="sectionbody">
|
| <div class="paragraph"><p>tesseract(1), wordlist2dawg(1), cntraining(1), mftraining(1), unicharset(5),
|
| unicharambigs(5)</p></div>
|
| </div>
|
| </div>
|
| <div class="sect1">
|
| <h2 id="_copying">COPYING</h2>
|
| <div class="sectionbody">
|
| <div class="paragraph"><p>Copyright (C) 2009, Google Inc.
|
| Licensed under the Apache License, Version 2.0</p></div>
|
| </div>
|
| </div>
|
| <div class="sect1">
|
| <h2 id="_author">AUTHOR</h2>
|
| <div class="sectionbody">
|
| <div class="paragraph"><p>The Tesseract OCR engine was written by Ray Smith and his research groups
|
| at Hewlett Packard (1985-1995) and Google (2006-2018).</p></div>
|
| </div>
|
| </div>
|
| </div>
|
| <div id="footnotes"><hr></div>
|
| <div id="footer">
|
| <div id="footer-text">
|
| Last updated
|
| 2024-05-19 13:04:22 CEST
|
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